Digital pictorial book system, a pictorial book searching method, and a machine readable medium storing thereon a pictorial book searching program

ABSTRACT

A digital pictorial book system for searching for and providing a user with an explanation of an object captured by an image capturing module includes an image capturing module for capturing an image; a main object selecting module for selecting a main object out of the image; a feature extracting module for extracting a feature of the main object; an explanation searching module for searching for the explanation of the main object in a plurality of image databases, which store explanations of the objects corresponded to a plurality of different kinds of features of the objects by using the feature extracted by the feature extracting module; and a distinguishing feature selecting module for selecting a distinguishing feature, of which an overlap of certainty distributions is the smallest for each object and each kind of the feature out of the different kinds of features stored in the image database corresponded to the plurality of objects in case the explanations of the plurality of objects are searched out by the explanation searching module, in order to narrow down the candidates even in case that a plurality of candidates is searched out in the image databases.

This patent application claims priority from a Japanese patentapplication No. 2004-44297 filed on Feb. 20, 2004 and 2004-372354 filedon Dec. 22, 2004, the contents of which are incorporated herein byreference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a digital pictorial book system, apictorial book searching method, and a machine readable medium storingthereon a pictorial book searching program. More particularly, thepresent invention relates to a digital pictorial book system, apictorial book searching method, and a machine readable medium storingthereon a pictorial book searching program for searching for andproviding a user with an explanation of an object of which an image iscaptured.

2. Description of the Related Art

Conventionally, a digital pictorial book system is known which extractsa feature of an object from an image of the object captured by using adigital camera, searches for and provides a user with information on theobject in an image database on the basis of the extracted feature (forexample, see Japanese Patent Application Laid-Open No. 1998-254901).

According to the conventional digital pictorial book system, forexample, a candidate of a name of the object is searched out in an imagedatabase by comparing a digitized image of the object with a digitizedimage of each category stored in the image database.

In case the captured image is compared with the image stored in theimage database, the search result is largely dependent on the differencein the image capturing condition. Therefore, the conventional digitalpictorial book system tries to provide a user with convenience by makingit easy to get the search result by increasing ambiguity of comparison.In this case, however, there is not known an efficient method fornarrowing down the candidates when various candidates are searched outaccording to the conventional digital pictorial book system.

SUMMARY OF THE INVENTION

Therefore, it is an object of the present invention to provide a digitalpictorial book system, a pictorial book searching method, and a machinereadable medium storing thereon a pictorial book searching program,which is capable of overcoming the above drawbacks accompanying theconventional art. The above and other objects can be achieved bycombinations described in the independent claims. The dependent claimsdefine further advantageous and exemplary combinations of the presentinvention.

According to the first aspect of the present invention, a digitalpictorial book system for searching for and providing a user with anexplanation of an object captured by an image capturing module includesan image capturing module for capturing an image; a main objectselecting module for selecting a main object out of the image; a featureextracting module for extracting a feature of the main object; anexplanation searching module for searching for the explanation of themain object in a plurality of image databases, which store explanationsof the objects corresponded to a plurality of different kinds offeatures of the objects by using the feature extracted by the featureextracting module; and a distinguishing feature selecting module forselecting a distinguishing feature, of which an overlap of certaintydistributions is the smallest for each object and each kind of thefeature out of the different kinds of features stored in the imagedatabase corresponded to the plurality of objects in case theexplanations of the plurality of objects are searched out by theexplanation searching module.

The digital pictorial book system may further include a second featureextracting module for extracting a feature of an object other than themain object out of the image, wherein the explanation searching modulemay search for the explanation of the main object by using the featureextracted by said second feature extracting module in the plurality ofimage databases which store the explanation of the object furthercorresponded to the feature of a thing of high relevance with theobject. The digital pictorial book system may further include annexedinformation acquiring module for acquiring annexed information annexedto the image, wherein explanation searching module may search for theexplanation of the main object by using the annexed information acquiredby the annexed information acquiring module in the plurality of imagedatabases which store the explanation of the object further correspondedto the annexed information corresponded to the image of the object.

The digital pictorial book system may further include an informingmodule for informing the user of the digital pictorial book system, of acontent of the distinguishing feature selected by the distinguishingfeature selecting module. The distinguishing feature may show a part ofthe object, and the informing module may inform the user that the partof the main object should be captured. The digital pictorial book systemmay further include a display module for displaying the image capturedby the image capturing module, wherein the image database may include animage of each part of the object as a feature, and the informing moduledisplays the image of the part, which is the distinguishing feature, onthe display module. The digital pictorial book system may furtherinclude a featuring part searching module for searching for the imageshowing the part of the main object, wherein the informing module maydisplay a frame which surrounds the part on the displaying module.

The informing module may inform the user of information showing an imagecapturing method for capturing the image including the part by usingsaid image capturing module in case the image showing the part issearched out in the main object by the featuring part searching module.The information showing the image capturing method may includeinformation showing the image capturing direction of said imagecapturing module. The information showing the image capturing method mayinclude information showing a position of said image capturing module.The information showing the image capturing method may includeinformation showing image capturing magnification of the image capturingmodule. The informing module may inform the user of the digitalpictorial book system of the content of the distinguishing featureselected by the distinguishing feature selecting module by using avoice.

The digital pictorial book system may further include an image capturingcontrol module for controlling the operation of the image capturingmodule on the basis of the content of the distinguishing featureselected by the distinguishing feature selecting module. The digitalpictorial book system may further include a featuring part searchingmodule for searching for an image showing a part of the main object,wherein the distinguishing feature may show the part of the object, andthe image capturing control module may control the operation of theimage capturing module on the basis of the image showing the partsearched out by the featuring part searching module. The image capturingcontrol module may control the image capturing direction of the imagecapturing module in order for the part to be included in the imagecapturing range of the image capturing module in case the image showingthe part is not searched out by the featuring part searching module inthe main object. The image capturing control module may control theposition of the image capturing module in order for the part to beincluded in the image capturing range of the image capturing module incase the image showing the part is not searched out by the featuringpart searching module in the main object. The image capturing controlmodule may increase the image capturing magnification of the imagecapturing module in case a ratio of a size of the image showing the partsearched out by the featuring part searching module to that of the wholeimage captured by said image capturing module is smaller than apredetermined reference value.

The image database may further store a dangerous thing information,which shows whether or not the object is a highly dangerous thing,corresponded to a plurality of different kinds of features of theobjects, and the informing module may inform the user that the object isa highly dangerous thing in case it is shown by the dangerous thinginformation corresponded to the object of which the explanation issearched out by the explanation searching module.

According to the second aspect of the present invention, a digitalpictorial book searching method performed by a digital pictorial booksystem for searching for and providing a user with an explanation of anobject captured by an image capturing module includes an image capturingstep of capturing an image; a main object selecting step of selecting amain object out of the image; a feature extracting step of extracting afeature of the main object; an explanation searching step of searchingfor the explanation of the main object in a plurality of imagedatabases, which store explanations of the objects corresponded to aplurality of different kinds of features of the objects by using thefeature extracted by the feature extracting step; a distinguishingfeature selecting step of selecting a distinguishing feature, of whichan overlap of certainty distributions is the smallest for each objectand each kind of the feature out of the different kinds of featuresstored in the image database corresponded to the plurality of objects incase the explanations of the plurality of objects are searched out bythe explanation searching step; and an informing step of informing theuser of the digital pictorial book system, of a content of thedistinguishing feature selected by the distinguishing feature selectingstep. The digital pictorial book searching method may further include afeaturing part searching step of searching for the image showing thepart of the main object, wherein, during the informing step, a framewhich surrounds the part is displayed in the displaying step.

According to the third aspect of the present invention, a machinereadable medium storing thereon a computer program making a computerperform as a digital pictorial book system for searching for andproviding a user with an explanation of an object captured by an imagecapturing module, the digital pictorial book system includes an imagecapturing module for capturing an image; a main object selecting modulefor selecting a main object out of the image; a feature extractingmodule for extracting a feature of the main object; an explanationsearching module for searching for the explanation of the main object ina plurality of image databases, which store explanations of the objectscorresponded to a plurality of different kinds of features of theobjects by using the feature extracted by the feature extracting module;a distinguishing feature selecting module for selecting a distinguishingfeature, of which an overlap of certainty distributions is the smallestfor each object and each kind of the feature out of the different kindsof features stored in the image database corresponded to the pluralityof objects in case the explanations of the plurality of objects aresearched out by the explanation searching module; and an informingmodule for informing the user of the digital pictorial book system, of acontent of the distinguishing feature selected by the distinguishingfeature selecting module. The machine readable medium may furtherinclude a featuring part searching module for searching for the imageshowing the part of the main object, wherein the informing module maydisplay a frame which surrounds the part on the displaying module.

The summary of the invention does not necessarily describe all necessaryfeatures of the present invention. The present invention may also be asub-combination of the features described above. The above and otherfeatures and advantages of the present invention will become moreapparent from the following description of the embodiments taken inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram exemplary showing the configuration of adigital pictorial book system 10 according to an embodiment of thepresent invention.

FIG. 2 is a block diagram exemplary showing the configuration of animage capturing unit 25 according to the embodiment of the presentinvention.

FIG. 3 shows an example of the process by a focal length acquiringmodule 144 according to the embodiment of the present invention.

FIG. 4 shows an example of weighting used by a main object distanceacquiring module 146 according to the embodiment of the presentinvention.

FIG. 5 shows an example of the exterior of the digital pictorial booksystem 10 according to the embodiment of the present invention.

FIG. 6 is a flowchart exemplary showing the process by an imagecapturing apparatus 20 according to the embodiment of the presentinvention.

FIG. 7 is a flowchart which shows S1010 in detail.

FIG. 8 is a flow chart which shows S1020 and S1030 in detail.

FIG. 9 is a flow chart which shows S1040 in detail.

FIG. 10 is a block diagram exemplary showing a partial FIG. 30 accordingto an embodiment of the present invention in detail.

FIG. 11 is a block diagram which shows an image database selectingmodule 320 and a candidate name searching module 340 according to theembodiment of the present invention in detail.

FIG. 12 shows an example of the process by a feature extracting module300 according to the embodiment of the present invention.

FIG. 13 shows a first example of an image database 38 according to theembodiment of the present invention.

FIG. 14 shows a second example of the image database 38 according to theembodiment of the present invention.

FIG. 15 shows a third example of the image database 38 according to theembodiment of the present invention.

FIG. 16 is a flowchart exemplary showing the process by a pictorial bookprocessing module 32 according to the embodiment of the presentinvention.

FIG. 17 is a block diagram exemplary showing a name determining unit 360according to the embodiment of the present invention in detail.

FIG. 18 shows an example of the process by the name determining unit 360according to the embodiment of the present invention.

FIG. 19 is a flowchart exemplary showing a process flow by the namedetermining unit 360 according to the embodiment of the presentinvention.

FIG. 20 is a block diagram exemplary showing an informing unit 380according to the embodiment of the present invention in detail.

FIG. 21 shows an example of certainty distributions of features storedin the image database 38 according to the embodiment of the presentinvention.

FIG. 22 shows a first example of the process of informing by aninforming module 386 according to the embodiment of the presentinvention.

FIG. 23 shows a second example of the process of informing by theinforming module 386 according to the embodiment of the presentinvention.

FIG. 24 shows a third example of the process of informing by theinforming module 386 according to the embodiment of the presentinvention.

FIG. 25 is a flowchart exemplary showing a process flow by the informingunit 380 according to the embodiment of the present invention.

FIG. 26 is a block diagram exemplary showing the hardware configurationof a personal computer 70 performing a function as the digital pictorialbook system 10 according to the embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The invention will now be described based on the preferred embodiments,which do not intend to limit the scope of the present invention, butexemplify the invention. All of the features and the combinationsthereof described in the embodiment are not necessarily essential to theinvention.

FIG. 1 is a block diagram to show an example of the configuration of adigital pictorial book system 10 according to an embodiment of thepresent invention. The digital pictorial book system 10 includes animage capturing apparatus 20 for capturing an image of an object andselecting a main object of the image, a pictorial book processing module32 for searching for an explanation on the object on the basis of thecaptured image of the object, and a communication module 34 forexchanging information with the outside. The image capturing apparatus20 includes an image capturing unit 25, a display module 50, such as anLCD monitor, for displaying and providing the captured image and theexplanation on the object to a user, a frame display module 40 fordisplaying a frame surrounding, for example, the main object, etc., onthe display module 50 so that the frame is superimposed on the capturedimage, and a second memory for storing the captured image, for example,a non-volatile memory such as a flash memory. The image capturingapparatus may be a digital still camera for taking still pictures or adigital video camera for taking moving pictures.

The digital pictorial book system 10 according to the embodiment of thepresent invention may be set by an operation mode changing switchoperated by the user, which is not shown, so that its operation mode canbe switched into a “digital pictorial book mode” in which the digitalpictorial book system 10 functions as a digital pictorial book forsearching for the information on the object captured by the imagecapturing apparatus 20 and providing the user with the information and“a digital camera mode” in which the digital pictorial book system 10functions as the so-called conventional digital camera for displayingthe object's image captured by the image capturing apparatus 20 by thedisplay module 50 and storing the image in the second memory 60.

FIG. 2 is a block diagram to show an example of the configuration of theimage capturing unit 25 according to the embodiment of the presentinvention. The image capturing unit 25 includes an image capturingmodule 100, a first memory 110, an image capture controlling module 120,an image processing module 130, and a variation operation module 150.

The image capturing apparatus 20 according to the present embodimentrecognizes a main object from the image captured by the image capturingmodule 100. It is an object of the image capturing apparatus 20 tocapture an image, which the user desires to obtain, easily and with agood quality by controlling the image processing based on the image ofthe recognized main object and displaying the recognized main objectwhich is surrounded by a frame to deliver to the user.

Further, in case the mode of the digital pictorial book system 10 is the“digital pictorial book mode,” it is an object to execute the searchefficiently and precisely by automatically recognizing the main objecton which information is to be searched out.

The image capturing module 100 includes an optical system 102, a CCD104, and a capturing signal processing module 106, and captures theimage of the object. The optical system 102 includes, for example, afocus lens, a zoom lens, and the like, and forms an image of the objecton a light receiving surface of the CCD 104. Further, the optical system102 can vary a focal length that is a distance from the image capturingapparatus 20 to the focused object by, for example, moving the focuslens. The CCD 104 includes a plurality of light receiving elements andoutputs electric charges, which are accumulated on each of the lightreceiving elements due to the object's optical image formed on the lightreceiving surface by the optical system 102, as a voltage signal to thecapturing signal processing module 106. Further, the CCD 104 can controla degree of exposure by controlling the time taken for the electriccharges to be accumulated on each of the light receiving elements.

The capturing signal processing module 106 decomposes the analog voltagesignal, which shows the object and is received from the CCD 104, into R,G, and B components. Then, the capturing signal processing module 106regulates white balance of the object by regulating each of the R, G,and B components. Further, the capturing signal processing module 106may execute a process such as gamma correction. Then, the capturingsignal processing module 106 converts the analog signal decomposed intothe R, G, and B components into a digital signal and outputs theacquired digital image data showing the object to the first memory 110.The first memory 110 is a volatile memory such as a DRAM and stores thedigital image data output from the capturing signal processing module106.

The image capture controlling module 120 controls the image showing theobject by driving a mechanical element included in the image capturingmodule 100. The image capture controlling module 120 includes anautomatic focusing module 122, an exposure selecting module 124, and awhite balance setting module 126. The automatic focusing module 122controls the focal length of the optical system 102. For example, theautomatic focusing module 122 controls the focal length of the opticalsystem 102 by driving the focus lens using a stepping motor.

The exposure selecting module 124 controls the degree to which theobject is exposed. Specifically, the exposure selecting module 124controls the degree of exposure by controlling the time taken forelectric charges to be accumulated on the light receiving elementsincluded in the CCD 104. Instead, the selecting module 124 may controlthe degree of exposure by controlling a mechanical shutter included inthe image capturing module 100, which is not shown. The white balancesetting module 126 sets white balance of the image showing the objectfor the image capturing module. Specifically, the white balance settingmodule 126 sets the white balance by controlling the regulating processof the R, G, and B components for the capturing signal processing module106. Further, the image capture controlling module 120 may control zoomand stop operations of the image capturing module 100.

The image processing module 130 processes the digital image data storedin the first memory 110 and outputs the processed result to the displaymodule 50 and the second memory 60. Alternatively, in case the operationmode of the digital pictorial book system 10 is the “digital pictorialbook mode,” the image processing module 130 outputs the processed resultto the pictorial book processing module. In this case, the image dataprocess is, for example, a data compression process such as an YCconverting process, JPEG (Joint Photographic Coding Experts Group), andthe like, and a process of converting to video signal such as NTSC, PAL,and the like. Further, the image processing module 130 includes a mainobject selecting module 140.

The main object selecting module 140 selects the main object out of theimages captured by the image capturing module 100 and received from thecapturing signal processing module 106. The main object selecting module140 includes a repeatedly capturing module 142, a focal length acquiringmodule 146, and a region selecting module 148. The repeatedly capturingmodule 142 makes the optical system 102 vary the focal length andcapture the images repeatedly, receives the captured images from thecapturing signal processing module 106, and outputs the received imagesto the focal length acquiring module 144. The focal length acquiringmodule 144 acquires the focal length at which an image can be acquired,each of the regions included in the image being best focused, on thebasis of the images captured by the repeatedly capturing module 142, andoutputs the acquired result to the main object distance acquiring module146.

The main object distance acquiring module 146 acquires a main objectdistance that is a distance to the main object on the basis of the focallength acquired by the focal length acquiring module 144, and outputsthe acquired result to the region selecting module 148. The regionselecting module 148 selects the region which is apart by the mainobject distance from the captured image as the main object. Then, theregion selecting module 148 outputs information on the selected regionto the frame display module 40, the automatic focusing module 122, theexposure selecting module, and the white balance setting module 126.Further, in case that the operation mode of the digital pictorial booksystem 10 is the “digital pictorial book mode,” the region selectingmodule 148 outputs the information on the selected region to thepictorial book processing module 32.

The frame display module 40 shown in FIG. 1 displays the framesurrounding the main object selected by the main object selecting module140 on the display module 50 so that the frame is superimposed on theimage captured by the image capturing module 100. Specifically, theframe display module 40 displays the frame surrounding the regionselected by the region selecting module 148 on the display module 50.The variation operation module 150 varies the main object distance to beoutput from the main object distance acquiring module 146 on the basisof the operation by the user of the image capturing apparatus 20.

According to the image capturing apparatus 20 of the present embodiment,it is possible to automatically recognize the main object from the imagecaptured by the image capturing module 100. Further, it is possible toeasily inform the user of the result of the recognition by displayingthe frame surrounding the recognized main object.

FIG. 3 shows an example of the process by the focal length acquiringmodule 144 according to the present embodiment. The focal lengthacquiring module 144 divides the image captured by the image capturingmodule 100 into a plurality of regions. For example, the focal lengthacquiring module 144 divides a captured image 200 into six rectangles inthe horizontal direction and into six rectangles in the verticaldirection. Alternatively, the focal length acquiring module 144 maydivide the captured image 200 into a plurality of regions havingdifferent shapes and areas from each other. In this case, the focallength acquiring module 144 can acquire precisely the focal length bydividing the captured image 200 into smaller regions, for example, nearthe center in which the probability of existence of the main object ishigh, while suppressing the load of the process, in comparison withdividing the entire of the captured image 200 into small regionsuniformly.

Further, the focal length acquiring module 144 may control the methodfor dividing the captured image into a plurality of regions on the basisof an image-capturing mode of the image capturing apparatus 20determined previously by the user. Here, the method for dividingindicates, for example, the number of the regions, the size and theshape of each of the divided regions, and the like. By controlling themethod for dividing the captured image on the basis of theimage-capturing mode like this, in case of selecting a portrait mode asthe image-capturing mode, it is possible to decrease the number of theregions and the time taken to acquire the focal length in comparisonwith selecting a conventional image-capturing mode. Further, in case ofselecting a mode of taking image at wider angle than the conventionalimage-capturing mode, it is possible to improve precision of focallength acquirement by increasing the number of regions.

The focal length acquiring module 144 detects the degree of focusing ofa partial image for each of the plurality of divided regions. Forexample, the focal length acquiring module 144 decomposes the imagesignal of each region into a plurality of frequency components by fastFourier transform (FFT) and the like, and detects the level of a highfrequency component for each region as the degree of focusing. Then, thefocal length acquiring module 144 detects an image, in which the degreeof focusing is highest at a certain region, out of the images capturedfor the plurality of focal lengths by the repeatedly capturing module142 and acquires the distance at which the image is captured as thefocal length of the region.

As above, according to the image capturing apparatus 20 of the presentembodiment, it is possible to acquire the focal length for each of theregions in the captured image with a high precision.

FIG. 4 shows an example of weighting used by the main object distanceacquiring module 146 according to the present embodiment. FIG. 4A showsan example of predetermined weighting for each region of an imagecaptured by the image capturing module 100. The main object distanceacquiring module 146 acquires the sum of products of the area of each ofthe regions having the same focal lengths and the predetermined weightof each region. Specifically, the main object distance acquiring module146 detects the plurality of regions having the same focal lengths as anobject existing at the focal length on the basis of the focal length ofeach region acquired by the focal length acquiring module 144. Then, foreach of the plurality of regions, the main object distance acquiringmodule 146 acquires a product of an area of the region and thepredetermined weight at the region, for example, shown in FIG. 4A. Themain object distance acquiring module 146 acquires the sum of theproducts acquired for the plurality of regions as weight of the objectexisting at the focal length.

In FIG. 4A, the weight of each region at the central portion of theimage is higher than the weight of each region around the image. That isbecause a composition of putting a main object at the central portion ofan image is widely used. The weight is shown in the present figure as anexample and not limited to the description on the present figure. Forexample, the weight is not constant generally and may vary according tothe capturing condition. Specifically, in case the image-capturing modeof the image capturing apparatus 20 is the portrait mode, the weight atthe central portion of the image may become larger than that of FIG. 4A.

FIG. 4B shows an example of relationship between the distance weight andthe focal length of the image captured by the image capturing module100. The main object distance acquiring module 146 acquires a value ofthe sum of the weight at the plurality of regions having the same focallengths acquired beforehand multiplied by the distance weight for thefocal length, which is, for example, obtained from FIG. 4B. Then, themain object distance acquiring module 146 acquires the focal length, atwhich the acquired value is the largest, as the main object distance.

According to FIG. 4B, the distance weight becomes smaller as the focallength becomes larger. That is because the main object is generally putthe nearer to the image capturing apparatus 20. The distance weight isshown in the present figure as an example and not limited to thedescription on the present figure. For example, the weight is notconstant generally and may vary according to the capturing condition.Specifically, in case the image-capturing mode of the image capturingapparatus 20 is the macro mode, the degree of the decrease in thedistance weight according to the increase in the focal length may becomelarger. Further, the distance weight at a typical focal length of anyimage-capturing mode may be made to be the largest.

According to the image capturing apparatus 20 of the present embodiment,it is possible to recognize the main object with a high precision byacquiring the main object distance making use of the weight at eachregion and each focal length.

FIG. 5 shows an example of the exterior of the digital pictorial booksystem 10 according to the embodiment of the present invention. FIG. 5Ashows an example of the exterior of the digital pictorial book system 10at a point of time. The pictorial book system 10 shown in FIG. 5Aincludes a long distance button 202 and a short distance button 204. Thelong distance button 202 and the short distance button 204 are anexample of the variation operation module 150.

The region selecting module 148 selects the region, which is apart bythe main object distance acquired by the main object distance acquiringmodule 146, from the image captured by the image capturing module 100 asthe main object. Then, the frame display unit 40 displays a frame 206surrounding the region selected by the region selecting module 148 sothat the frame 206 is overlapped with the captured image displayed bythe display module 40. Specifically, the frame display module 40displays the frame 206 in the neighborhood of and around thecircumference of the main object. Here, the neighborhood of thecircumference of the main object is not limited to the circumference ofthe region showing the main object itself. For example, the framedisplay module 40 may display the frame 206 extended to the outside by apredetermined pixel with respect to the circumference. Thus, it ispossible to prevent the main object that is an important region in thecaptured image from coming difficult to see.

FIG. 5B shows an example of the exterior of the digital pictorial booksystem 10 in case the user operates the long distance button 202. Incase the user pushes the long distance button 202, the long distancebutton 202 instructs the main object distance acquiring module 146 tovary the main object distance farther. Further, in case the user pushesthe close distance button 204, the long distance button 204 instructsthe main object distance acquiring module 146 to vary the main objectdistance more closely.

The main object distance acquiring module 146 receives the instruction,and, out of a plurality of candidates of the main object distance, whichare selected on the basis of the weight of the object for each focallength, selects one candidate further away from or closer to the mainobject at this point in time as a new main object distance. The mainobject distance acquiring module 146 outputs the result to the regionselecting module 148. Then, the region selecting module 148 selects theregion which is apart by the new main object distance as a main objectand outputs information on the region to the frame display module 40.The frame display module 40 receives the information and displays aframe 208 on the display module 50, instead of the frame 206.

According to the image pick apparatus 20 of the present embodiment, evenif the automatically recognized main object is not correct, the user canexecute image-capturing as intended by selecting a correct main objectdistance.

Further, in case of taking moving pictures by the image capturingapparatus 20, it is possible to change the main object without changingcomposition while taking images, by varying the main object distancemaking use of the variation operation module 150.

FIG. 6 is a flowchart to show an example of the process by the imagecapturing apparatus 20 according to the present embodiment. In case therelease switch is half-pressed by the user, the image capturingapparatus 20 starts the following processes (S1000). Alternatively, theimage capturing apparatus 20 may start the following processes wheneverelectric power is input. The image capturing apparatus captures an imageof an object by the image capturing module 100 and selects a main objectfrom the image (S1010) Then, the image capturing apparatus 20 controlsthe image capturing of the image capturing module 100 by the imagecapture controlling module 120 based on the selected main object(S1020). The image capturing apparatus 20 captures an image of theobject and displays on the display module 50 (S1030).

The image capturing apparatus 20 varies the main object distance basedon the operation by the user (S1040). In case the release switch isfully pressed, the image capturing apparatus 20 captures an image of theobject by the image capturing module 100 and stores the captured imagein the first memory 110. Then, the image capturing apparatus 20 executesan image processing such as a data compression process and stores theimage data in the second memory 60 (S1050). Here, in case the operationmode of the digital pictorial book system 10 is the “digital pictorialbook mode,” the image capturing apparatus 20 outputs the image data andinformation showing the region of the selected main object to thepictorial book processing module 32, instead of storing the image datain the second memory 60.

FIG. 7 is a flowchart to show S1010 in detail. The repeatedly capturingmodule 142 executes the following processes for each of a plurality ofpredetermined focal lengths (S1100) Here, the predetermined focallengths are selected from a focusing range of the optical system 102 andnecessary for acquiring the main object distance. The focal lengths maybe fixed or varied according to the image-capturing mode of the imagecapturing apparatus 20. For example, in case the image-capturing mode isthe portrait mode, the image capturing apparatus 20 may select morefocal lengths from a region near to the image capturing apparatus 20than a region far away from the image capturing apparatus 20.

Further, the repeatedly capturing module 142 may display images of 30frames per a second by the display module 50 and acquire the focallength by using the other images out of the images captured at afrequency higher than 30 frames per a second. Thus, it is possible tocapture images at a plurality of focal lengths while displaying theimages with a constant focal length to the user.

The repeatedly capturing module 142 varies the focal length of theoptical system 102 (S1110). The image capturing module 100 captures animage of the object and stores the image data in the first memory 110(S1120). The focal length acquiring module 144 divides the capturedimage into a plurality of regions and acquires focusing degree for eachof the regions (S1130) The image capturing apparatus 20 repeats S1110 toS1130 for each of the plurality of the predetermined focal lengths(S1140).

For each of the regions, the main object distance acquiring module 146acquires the focal length of the highest focusing degree of theplurality of focal lengths, at which the images are captured, as thefocal length of the region (S1150). The main object distance acquiringmodule 146 detects a plurality of regions having the same focal lengthsout of the regions and acquires the sum of products of the area of eachregion and the predetermined weight for each region. Then, the mainobject distance acquiring module 146 calculates the sum of the productsacquired for each region. The main object distance acquiring module 146acquires the value of multiplying the calculated sum by the weightpredetermined for the corresponding focal length for each of the focallengths. The main object distance acquiring module 146 executes thefollowing processes for each group of the regions having the same focallengths, selects a plurality of focal lengths in the descending order ofthe acquired values, and selects the focal length of the largest valueas the main object focal length and the other focal lengths ascandidates for the main object focal length (S1160). The regionselecting module 148 detects a plurality of regions having the mainobject focal length as the main object (S1170).

FIG. 8 is a flow chart to show S1020 and S1030 in detail. The automaticfocusing module 122 controls the optical system 102 to bring the regionselected by the region selecting module 148, that is the main object,into focus (S1200). Further, the exposure selecting module 124 selectsthe degree of exposure of the image which gives bigger weight to theregion selected by the region selecting module 148, that is, the mainobject, than the other regions (S1210). Furthermore, the white balancesetting module 126 sets the white balance of the image which givesbigger weight to the region selected by the region selecting module 148,that is, the main object, than the other regions (S1220).

The image capturing module 100 captures an image of the object under thecapturing conditions such as the focus, the degree of exposure, and thewhite balance controlled on the basis of the selected main object(S1230). The display module 50 displays the captured image (S1240). Theframe display module 40 displays a frame surrounding the region selectedby the region selecting module 148 on the display module 50 so that theframe is overlapped with the captured image (S1250).

FIG. 9 is a flow chart to show S1040 in detail. The image capturingapparatus 20 determines whether or not the release switch is fullypressed by the user (S1300) In case the release switch is not fullypressed (S1300: No), the image capturing apparatus 20 determines whetheror not the main object distance is varied by the variation operationmodule 150 (S1310). In case the main object distance is not varied(S1310: No), the image capturing apparatus 20 returns to S1300 anddetermines again whether or not the release switch is fully pressed.

In case the main object distance is varied (S1310: Yes), that is, theuser changes the main object distance with one of the candidates for themain object distance in the main object distance acquiring module 146 byusing the variation operation module 150, the main object distanceacquiring module 146 outputs the changed main object distance to theregion selecting module 148. Then, the region selecting module 148selects again the region which is apart by the changed main objectdistance (S1170).

The automatic focusing module 122 drives the optical system 102 so as tofocus at the changed main object distance (S1200). Further, the exposureselecting module 124 selects the degree of exposure of the image whichgives bigger weight to the region selected according to the changed mainobject distance, that is, the main object, than the other regions(S1210) Furthermore, the white balance setting module 126 sets the whitebalance of the image which gives bigger weight to the region selectedaccording to the changed main object distance, that is, the main object,than the other regions (S1220).

The image capturing module 100 captures an image of the object under thecapturing conditions such as the focus, the degree of exposure, and thewhite balance adjusted on the basis of the changed main object distance(S1230). The display module 50 displays the captured image (S1240).Further, the frame display module 40 displays a frame surrounding theregion selected on the basis of the changed main object distance on thedisplay module 50 to inform the user that the main object is changed(S1250).

On the other hand, in case the release switch is fully pressed (S1300:Yes), the image capturing apparatus 20 proceeds to S1050 and captures animage of the object.

According to the image capturing apparatus 20 of the present embodiment,it is possible to capture images, which the user desires to obtain, witha high precision by determining the capturing conditions such as thefocus, the degree of exposure, and the white balance on the basis of themain object recognized by the main object selecting module 140 tocontrol the image capturing module 100.

FIG. 10 is a block diagram to show an example of a partial FIG. 30according to an embodiment of the present invention in detail. Thepictorial book processing module 32 included in the digital pictorialbook system 10 connects with a network 36. Then, the digital pictorialbook system 10 searches for an explanation on the object in a pluralityof databases (38 a, 38 b, . . . , 38 c; hereinafter, referred to 38),which stores information on the object corresponded each of a pluralityof features of the object different from each other, through the network36. Here, the network 36 is, for example, the Internet. Further, theimage database 38 may be, for example, one image database providingservice or a plurality of image databases services different from eachother.

It is an object of the pictorial book processing module 32 and thecommunication module 34 according to the present embodiment to getsearch result efficiently and precisely in case of searching for anexplanation on the object by using a plurality of features.

The pictorial book processing module 32 includes a first featureextracting module 300, a second feature extracting module 302, anannexed information acquiring module 304, a feature selecting module310, an image database selecting module 320, an explanation searchingmodule 330, and an informing unit 380. The first feature acquiringmodule 300 receives the object's image captured by the image capturingmodule 100 and the region showing the main object which is selected bythe main object selecting module 140 from the image processing module130 shown in FIG. 2. Then, the first feature extracting module 300extracts features of the main object from the received image and outputsthem to the feature selecting module 310. Here, the feature of the mainobject is, for example, a contour shape, a digitized image, adistribution of color, and the like. The second feature extractingmodule 302 receives the image of the object captured by the imagecapturing module 100 and the region showing the main object selected bythe main object selecting module 140 from the image processing module130. Then, the second feature extracting module 302 extracts features ofan object other than the main object from the received image and outputsthem to the feature selecting module 310.

The annexed information acquiring module 304 obtains the object's imagecaptured by the image capturing module 100 and information annexed tothe image from the image processing module 130. Here, the annexedinformation may include, for example, latitude, longitude, altitude, anddepth of the place where the image is captured, acquired by GPS (GlobalPositioning System) included in the image capturing apparatus 20.Further, the annexed information may include, for example, the captureday and/or time acquired by a calendar function of the image capturingapparatus 20 and information on a season based on the day and time.Furthermore, the annexed information may include, for example, atemperature acquired by a thermometer included in the image capturingapparatus 20. Furthermore, the annexed information may include, forexample, a distance from the image capturing apparatus 20 to the mainobject, the size of the main object in the image, and information on thereal size of the main object acquired by using an image capturingmagnification of the image capturing apparatus 20 and the like. Then,the annexed information acquiring module 304 outputs the acquiredannexed information to the feature selecting module 310.

The feature selecting module 310 selects a feature of a predeterminedkind out of the features of all kinds extracted from the main object bythe first feature extracting module 300. Then, the feature selectingmodule 310 outputs each of the feature of the predetermined kind and afeature not selected to the image database selecting module 320.Further, the feature selecting module 310 selects a feature of apredetermined kind out of features of all kinds extracted from an objectother than the main object by the second feature extracting module 302.The feature extracting module 310 outputs each of the feature of thepredetermined kind and a feature not selected to the image databaseselecting module 320. Further, the feature selecting module 310 selectsannexed information of a predetermined kind out of annexed informationof all kinds. Then, the feature selecting module 310 outputs each of theannexed information of the predetermined kind and annexed informationnot selected to the image database selecting module 320.

Further, the image selecting module 310 may select features of at leasta part of kinds out of the features of all the kinds as the feature ofthe predetermined kind or the features of another kind, instead ofselecting the features of all the kinds extracted from the main objectby the first feature extracting module 300 as the feature of thepredetermined kind or the feature of another kind. Specifically, thefeature extracting module 310 may select the feature of thepredetermined kind out of the features of at least a part of kinds whichare extracted based on the certainty higher than a predetermined valuefrom the features of all the kinds extracted from the main object by thefirst feature extracting module 300, and select a feature of a kindother than the predetermined kind out of the features of at least a partof the kinds as a feature of another kind. Here, the probability of afeature may be a value indicating a certainty that an image showing themain object have the feature actually, for example, a value acquired byusing a method predetermined according to the kind of the feature, onthe basis of quality of image data used for extracting the feature andthe like. For example, the maximum value of difference between adjacentpixels may be acquired as a probability of a feature showing a contourshape of the object. Consequently, in case the acquired maximum value ofdifference between adjacent pixels is smaller than a value predeterminedby, for example, the user because the focusing degree of the capturedimage is not sufficiently high, it is difficult to say that theprobability of the feature showing the contour shape of the object isnot high and thus the feature selecting module 310 may exclude thefeature showing the contour shape of the object while selecting thefeature of the predetermined kind and the feature of another kind.

Further, similarly, the feature selecting module 310 may select featuresof a predetermined kind out of features of at least a part of kindswhich are extracted based on the certainty higher than a predeterminedvalue from the features of all the kinds extracted by the second featureextracting module 302, and select a feature of a kind other than thepredetermined kind out of the features of at least a part of the kindsas a feature of another kind. Alternatively, the feature selectingmodule 310 may select annexed information of a predetermined kind out ofannexed information of at least a part of kinds which are acquired basedon the certainty higher than a predetermined value out of the annexedinformation of all the kinds acquired by the annexed informationacquiring module 304, and select annexed information of a kind otherthan the predetermined kind out of the annexed information of at least apart of the kinds as annexed information of another kind.

The image database selecting module 320 selects an image databasestoring the feature of the kind extracted by the first featureextracting module 300 on the basis of the information received from theplurality of image databases 38 through the communication module 34 outof the plurality of image databases 38. Here, the explanation of theobject may be the name of the object and comment on ecology in case theobject is an animal or a plant. Further, the explanation of the objectis not limited to letter information and may include variousinformation, such as image information including a reference image ofthe object, data on a three-dimensional shape, habitat, altitude, anddepth. The database selecting module 320 may select an image databasewhich further stores the feature of the kind extracted by the secondfeature extracting module 302 for the object other than the main objectout of a plurality of image databases storing explanations of objectsfurther corresponded to features of a thing having a high relevance withthe object. Further, the database selecting module 320 may select animage database which further stores the annexed information of the kindacquired by the annexed information acquiring module 304 out of aplurality of image databases storing explanations of objects furthercorresponded to information annexed to the captured image.

The explanation searching module 330 searches for explanation of themain object in the database 38 selected by the image database searchingmodule 320 by using a feature extracted by the first feature extractingmodule 300. Further, the explanation searching module 330 may search foran explanation of the main object in the database 38 selected by theimage database searching module 320 by using further a feature of anobject other than the main object extracted by the second featureextracting module 302. Furthermore, the explanation searching module 330may search for an explanation of the main object in the database 38selected by the image database searching module 320 by using furtherannexed information acquired by the annexed information acquiring module304. The explanation searching module 330 includes a candidate namesearching module 340, an overlapped object selecting module 350, a namedetermining module 360, and an explanation reading module 370.

The candidate name searching module 340 searches for a candidate namethat is a candidate for the name of the main object in each of theplurality of image databases 38 by using the features extracted by thefirst feature extracting module 300 and the second feature extractingmodule 302 and the annexed information acquired by the annexedinformation acquiring module 304 by communicating with the imagedatabase 38 through the communication module 34. Then, the candidatename searching module 340 outputs the result searched out to theoverlapped object selecting module 350, the name determining unit 360,and the informing unit 380. Although the candidate name searching module340 searches the candidate of the name of the main object in the presentembodiment, a thing to be searched for is not limited to the candidateof the name and may be any information which makes the objectdistinguished. In addition to the candidate of the name of the mainobject, the candidate name searching module 340 may search anexplanation of the object corresponding to the name.

The overlapped object selecting module 350 selects an overlapped objectsearched out in a plurality of image databases on the basis of thesearch result received from the candidate name searching module 340.Specifically, the overlapped object selecting module 350 selects anobject having a name common to the plurality of image databases out ofthe names of a plurality of objects included in the result of searchingthe plurality of image databases, which is received from the candidatename searching module 340, as the overlapped object. Then, theoverlapped object selecting module 350 outputs the name of theoverlapped object to the explanation retrieving module 370.

The name determining unit 360 determines a most certain name as the mainobject on the basis of the result of searching the plurality ofimage-databases, which is received from the candidate name searchingmodule 340, and outputs the result to the explanation retrieving module370. The explanation retrieving module 370 retrieves an explanation ofthe main object stored in at least one image database 38, on the basisof the name received from at least one of the overlapped objectselecting module 350 and the name determining unit 360. Specifically,the explanation retrieving module 370 transmits the name of the mainobject to at least one of the image databases 38 by using thetransmitting module 400, makes information of the main object searchedfor in each of the image databases 38, and retrieves the explanation ofthe main object obtained as a result by using the receiving module 410.Then, the explanation retrieving module 370 displays and provides theretrieved explanation to the user by the display module 50. Theinforming unit 380 informs the user which portion of an object tocapture in case of further narrowing the result of searching theplurality of image databases 38 received from the candidate namesearching module 340.

The communication module 34 includes the transmitting module 400 and areceiving module 410. The transmitting module 400 is connected with theplurality of image databases 38 through the network 36 and transmitsinformation to the plurality of image databases 38 in response torequest of each module of the pictorial book processing module 32. Forexample, the transmitting module 400 transmits the features of the mainobject extracted by the first feature extracting module 300 to the imagedatabases 38 formed outside.

The receiving module 410 is connected to the plurality of imagedatabases 38 through the network 36 and transmits the informationreceived from the plurality of image databases 38 to each module of thepictorial book processing module 32. For example, the receiving module410 receives the explanation of the main object searched for in theimage database 38 through the network 36 and transmits the explanationto the candidate name searching module 340 or the explanation retrievingmodule 370. The transmitting module 400 and the receiving module 410connects with the network 36 by a wired communication such as Ethernet(registered trademark) or a wireless communication such as LAN,Bluetooth (registered trademark), and CDMA.

According to the digital pictorial book system 10 of the embodiment ofthe present invention, it is possible to achieve reduction of the sizeand the cost of the digital pictorial book system 10 by using the imagedatabase connected through the network 36.

Further, it is possible to obtain precise search result covering a widesearch range by searching various image databases connected to theInternet and the like.

Furthermore, according to the digital pictorial book of the embodimentof the present invention, it is possible to execute search by using onlya feature which is certain out of the features extracted from the image.For example, in case the main object is a flower, if the number of petaland the number of stamen of the main object cannot be recognized withsufficiently high precision because capturing conditions are notsufficiently good, it is possible to search explanation of the mainobject by using a feature other than the number of petal and the numberof stamen. Therefore, it is possible to improve precision of the search.

The configuration shown in the present figure is an example and it ispossible to make various changes. For example, the pictorial bookprocessing module 32 may not include at least one of the second featureextracting module 302 and the annexed information acquiring module 304.Further, the explanation searching module 330 may not include at leastone of the overlapped object selecting module 350 and the namedetermining module 360. In this case, the explanation retrieving module370 retrieves explanation of the main object from the image database 38on the basis of the name of the main object received from the overlappedobject selecting module 350 or the name determining module 360.Furthermore, the explanation searching module 330 may not include theinforming unit 380.

In addition, although the pictorial book processing module 32 shown inthe present figure accesses to the plurality of image databases formedoutside the digital pictorial book system 10 through the communicationmodule 32 and the network 36, the pictorial book processing module 32may access to a plurality of image databases formed inside the digitalpictorial book system 10. Thus, the pictorial book system 10 can searchan explanation of the main object in case communication with the outsideis impossible.

Further, the digital pictorial book system 10 can be controlled tochoose which one to use of the outside image database 38 and the insideimage database 38. In other words, the digital pictorial book system 10uses the plurality of image databases 38 formed inside if communicationwith the outside is impossible, and otherwise use the plurality of imagedatabases 38 formed outside otherwise. Furthermore, digital pictorialbook system 10 can use both the outside image database 38 and the insideimage database 38 at the same time. As described above, it is possibleto search an explanation of the main object efficiently by changingflexibly the configuration of the image database to be used according topurposes and conditions of utilization.

FIG. 11 is a block diagram to show the image database selecting module320 and the candidate name searching module 340 according to the presentembodiment in detail. The image database selecting module 320 includes afirst image database selecting module 322 and a second image databaseselecting module 324. The first image database selecting module 322receives information of the image database 38, for example, a kind offeature stored in each of the plurality of image databases 38, throughthe network 36 by using the receiving module 410. On the basis of theinformation, the first image database selecting module 322 selects afirst image database storing the feature of the predetermined kindselected by the feature selecting module 310 out of the featuresextracted by the first feature extracting module 300. Then, the firstimage database selecting module 322 outputs information showing thefirst image database to the candidate name searching module 340.

The second image database selecting module 324 receives information ofthe image database 38 through the network 36 by using the receivingmodule 410. On the basis of the information, the second image databaseselecting module 324 selects a second image database storing a featureof a kind other than the predetermined kind selected by the featureselecting module 310 out of the features extracted by the first featureextracting module 300. Then, the second image database selecting module324 outputs the information showing the second image database to thecandidate name searching module 340.

The candidate name searching module 340 includes a first searchingmodule 342 and a second searching module 344. The first searching module342 searches for a candidate for the name of the main object by usingthe feature of the predetermined kind selected by the feature selectingmodule 310 in the first image database selected by the first imagedatabase selecting module 322. Specifically, the first searching module342 transmits the feature of the predetermined kind to the first imagedatabase through the network 36 by using the transmitting module 400.Then, the first searching module 342 receives the result of searchingthe first image database through the network 36 by using the receivingmodule 410 and outputs the received candidate for the name of the mainobject to the overlapped object selecting module 350.

The second searching module 344 searches for the name of the main objectby using the feature of the kind other than the predetermined kindsselected by the feature selecting module 310 in the second imagedatabase selected by the second image database selecting module 324.Specifically, the second searching module 344 transmits the feature ofanother kind to the second image database through the network 36 byusing the transmitting module 400. Then, the second searching module 344receives the result of searching the second image database through thenetwork 36 by using the receiving module 410 and outputs the receivedexplanation of the main object to the overlapped object selecting module350.

The overlapped object selecting module 350 shown in FIG. 10 selects anoverlapped object which is searched out in both the first and secondimage databases on the basis of the name of the main object that is thesearch result received from each of the first searching module 342 andthe second searching module 344. Specifically, the overlapped objectselecting module 350 selects an object, which has having the same namesout of the plurality of objects included in the search result receivedfrom each of the first searching module 342 and the second searchingmodule 344, as the overlapped object. Then, the overlapped objectselecting module 350 outputs the name of the overlapped object as thename of the main object to the explanation retrieving module 370 show inFIG. 10.

The first image database selecting module 322 may select an imagedatabase 38, in which the first searching module 342 can search for anexplanation of the main object more precisely by using the feature ofthe predetermined kind extracted by the first feature extracting module300 and selected by the feature selecting module 310, with higherpriority out of the plurality of the databases 38 as the first imagedatabase. Specifically, the first image database selecting module 322may select an image database 38, which has more objects storing anexplanation corresponded to the feature of the predetermined kind thanother image databases, with higher priority out of the plurality ofimage databases 38 as the first image database.

Further, the second image database selecting module 324 may select animage database 38, in which the second searching module 344 can searchfor an explanation of the main object more precisely by using thefeature of another kind different from the predetermined kind extractedby the first feature extracting module 300 and selected by the featureselecting module 310, with higher priority out of the plurality of thedatabases 38 as the second image database. Specifically, the secondimage database selecting module 324 may select an image database 38,which has more objects storing an explanation corresponded to thefeature of another kind than other image databases, with higher priorityout of the plurality of image databases 38 as the second image database.Further, each of the first image database selecting module 322 and thesecond image database selecting module 324 may acquire the number ofobjects storing an explanation corresponded to the feature of the objectwith respect to each of the plurality of kinds of the features ofthrough the communication module 34 from each of the plurality of imagedatabases 38.

Furthermore, the configuration shown in the present figure is an exampleand various changes can be made to the configuration of the presentfigure. For example, although the pictorial book processing module 32searches for the explanation of the main object in the first imagedatabase by using the feature of the predetermine kind selected by thefeature selecting module 310 and the second image database by using thefeature of another kind, the pictorial book processing module 32 mayselect a group of features of an arbitrary number from the plurality offeatures extracted by the first feature extracting module 300 by usingthe feature selecting module 310 so as to execute search of one (1)image database or the databases more that three (3). Then, theoverlapped object selecting module 350 may selects an overlapped namefrom the result of searching for the plurality of databases 38 as thename of the main object and output the overlapped name to theexplanation retrieving module 370.

According to the pictorial book system 10 of the present embodiment, itis possible to select an image database to be searched among theplurality of image databases and search for an explanation of the mainobject on the basis of result of the search of the image database 38.Thus, appropriate load distribution can be accomplished without using aload distributing system such as the load balance for the conventionalinformation searching system. Thus, since the digital pictorial booksystem 10 can access directly to the image database 38, it is possibleto improve response speed of the search process.

Further, according to the digital pictorial book system 10, it ispossible to select an image database 38, in which an explanation of themain object is searched out more precisely by using the feature of thepredetermined kind selected by the feature selecting module 310, as thefirst image database and select an image database 38, in which anexplanation of the main object is searched out more precisely by usingthe feature of another kind, as the second image database. Thus, it ispossible to search for an explanation of the main object precisely byusing each of the features extracted from the image with respect to animage database 38 in which an explanation of the main object can besearched precisely for the kind of the feature, even if the precision ofthe search is subject to variation according to the kind of the feature,that is, it is possible to search for an explanation of the object incase of using a feature of a kind but it is impossible to search for anexplanation of the object in case of using a feature of another kind.

Furthermore, it is possible to search for an explanation of the mainobject precisely by using the image database selected by the first imagedatabase selecting module 322 and the second image database selectingmodule 324 by determining that an image database 38 in whichexplanations of more objects are searched out for a feature of a kind isthe image database 38 in which information of the object can be searchedout more precisely for the feature of the kind.

Furthermore, it is possible to select an appropriate image database 38and search for an explanation of the main object precisely by acquiringthe number of objects storing an explanation for each kind of thefeature and deciding the precision of search, even if various imagedatabases 38, for example, open on the Internet are used.

FIG. 12 shows an example of the process by the feature extracting module300 according to the present embodiment. FIG. 12A shows an image 200which the first feature extracting module 300 receives from the imageprocessing module 130. The first feature extracting module 300 extractsa feature of a main object from an image of the main object surroundedby a frame 222. Here, the first feature extracting module 300 may selecta feature to be extracted on the basis of information showing the sizeor the kind of the main object, which is input by the user beforehand,so as to reduce the time taken to search for an explanation and acquirecorrect search result. For example, in case the user judges the mainobject a flower, the user inputs information showing that the mainobject is a flower to the digital pictorial book system 10 by using akind input module not shown. Then, the first feature extracting module300 extracts a feature predetermined for each kind of the main object onthe basis of the input information.

For example, in case the user inputs that the main object is a flower,the first feature extracting module 300 executes a process of extractinga contour shape for the image data the main object surrounded by theframe 222. Then, the first feature extracting module 300 divides theimage data into a flower part and a stem and leaf part on the basis ofcolor distribution of the inside of the acquired contour shape. Then thefirst feature extracting module 300 extracts a color and a contour shapeof the flower as features.

FIG. 12B shows the contour shape of the flower in the main object of theimage 220 extracted by the first feature extracting module 300. Further,the first feature extracting module 300 extracts the number of the petalas a feature by executing shape recognition for the contour shape of theflower. In addition, the first feature extracting module 300 extractsthe number of the stamen as a feature by executing the shape recognitionof the flower part of the image data of the main object. Then, the firstfeature extracting module 300 outputs the extracted features to thefeature selecting module 310.

FIG. 13 shows a first example of the image database 38 according to thepresent embodiment. The feature selecting module 310 selects a part ofthe features extracted by the first feature extracting module 300. Forexample, the feature selecting module 310 selects the color and thecontour shape of the flower out of the color of the flower, the contourshape of the flower, the number of the petal, and the number of thestamen. Then, the feature selecting module 310 outputs informationshowing the selected features to the display module 320.

The image database selecting module 320 selects a first image databasestoring the color and the contour shape of the flower which are a partof the features selected by the feature selecting module 310 and asecond image database storing the number of the petal and the number ofthe stamen which are other features selected by the feature selectingmodule 310.

FIG. 13A shows an example of a first image database 226 selected by theimage database selecting module 320. FIG. 13B shows a second imagedatabase 228 selected by the image database selecting module 320. Thefirst searching module 342 searches for an object, of which the colorand the contour shape of the flower are white and the contour shape 224,respectively, in the first image database 226. The second searchingmodule 344 searches for an object, of which the number of the petal andthe number of the stamen are six (6) and six (6), respectively, in thesecond image database 228.

The overlapped object selecting module 350 selects, for example, “EasterLily” as an overlapped object, which is searched out by the firstsearching module 342 and the second searching module 344, out of theobjects selected by the first searching module 342 and the secondsearching module 344. Then, the explanation searching module 370retrieves an explanation of the “Easter Lily” selected by the overlappedobject selecting module 350 from the first image database 226 and thesecond image database 228 and displays on the display module 50.

According to the digital pictorial book system 10 of the presentembodiment, in case of searching for an explanation of the main objectin the plurality of image databases 38, the search results of the imagedatabases 38 are prevented from being similar each other by executingsearch on the basis of the features different for each of the pluralityof image databases 38. Thus, it is possible to narrow-down the searchresult efficiently and acquire the search result correctly.

FIG. 14 shows a second example of the image database 38 according to thepresent embodiment. The image database 38 may further store features ofa thing having high relevance with the object corresponded to theexplanations of the object, respectively, in addition to the features ofthe object stored in the image database 38 shown in FIG. 13. Forexample, in case the object is a flower, the image database 38 may storea feature showing the color of the ground on which the flower blooms asa feature of a thing having high relevance with the object. In thepresent figure, the image database 38 stores a quantity of featureshowing “Deep brown” corresponded to the explanation about the objectshowing “Easter Lily.” Further, the image database 38 may storequantities of feature showing the color of sandy soil, the color of arock, and the like, corresponded to explanations about the object otherthan “Easter Lily.”

The second feature extracting module 302 extracts a feature of an objectother than the main object selected by the main object selecting module140 from the image captured by the image capturing module 100. Forexample, the second feature extracting module 302 detects an objectshowing the ground on the basis of the position of the object in thescreen, color distribution of the object, or positional relationshipwith the main object, out of a plurality of objects which are includedin the captured image and extracted by a process of extracting contourshape. Then, the second feature extracting module 302 recognizes thecolor of the selected object showing the ground. For example, the secondfeature extracting module 302 may detect a color range having thehighest degree of focusing in color distribution of the object showingthe ground out of a plurality of predetermined color ranges.

Then, the feature selecting module 310 selects a feature of apredetermined kind out of a plurality of features of the main objectwhich are extracted by the first feature extracting module 300, andselects a feature of a predetermined kind out of a plurality of featuresof an object other than the main object which are extracted by thesecond feature extracting module 302. The first image database selectingmodule 322 selects an image database 38, which stores the feature of thepredetermined kind selected by the feature selecting module 310 out ofthe kinds of the features extracted by the first feature extractingmodule 300 and the feature of the predetermined kind selected by thefeature selecting module 310 out of the kinds of the features extractedby the second feature extracting module 302, as a first image database.The first image database selecting module 322 may select an imagedatabase 38, in which the first searching module 342 can search for anexplanation of the main object more precisely by using the feature ofthe predetermined kind extracted by the first feature extracting module300 and the feature of the predetermined kind extracted by the secondfeature extracting module 302, with higher priority out of the pluralityof the databases 38 as the first image database.

Further, the second image database selecting module 324 selects an imagedatabase 38, which stores a feature of a kind other than thepredetermined kind selected by the feature selecting module 310 out ofthe kinds of the features extracted by the first feature extractingmodule 300 and a feature of a kind other than the predetermined kindselected by the feature selecting module 310 out of the kinds of thefeatures extracted by the second feature extracting module 302, as asecond image database. The second image database selecting module 324may select an image database 38, in which the second searching module344 can search for an explanation of the main object more precisely byusing the feature of another kind extracted by the first featureextracting module 300 and the feature of another kind extracted by thesecond feature extracting module 302, with higher priority out of theplurality of the databases 38 as the second image database.

The first searching module 342 searches a candidate for the name of themain object from the first image database selected by the first imagedatabase selecting module 322 by using both the feature of thepredetermined kind selected by the feature selecting module 310 out ofthe kinds of the features extracted by the first feature extractingmodule 300 and the feature of the predetermined kind selected by thefeature selecting module 310 out of the kinds of the features extractedby the second feature extracting module 302. Further, the secondsearching module 344 searches a candidate for the name of the mainobject from the second image database selected by the second imagedatabase selecting module 324 by using both a feature of a kind otherthan the predetermined kind selected by the feature selecting module 310out of the kinds of the features extracted by the first featureextracting module 300 and a feature of a kind other than thepredetermined kind selected by the feature selecting module 310 out ofthe kinds of the features extracted by the second feature extractingmodule 302. As described with regard to FIG. 11, the overlapped objectselecting module 350 selects the name of the main object on the basis ofthe results of searching for the candidate for the name of the mainobject by each of the first searching module 342 and the secondsearching module 344 and outputs the name of the main object to theexplanation retrieving module 370.

According to the pictorial book system 10 of the embodiment of thepresent invention, it is possible to search for an explanation of themain object further on the basis of features of objects other than themain object which are an image of the background and an image of thingsexisting around in the captured image. Thus, for example, in case themain object is a plant, the main object can be specified on the basis ofmore information as a plant, which is the main object, can be specifiedon the basis of information showing an environment in which the mainobject grows up, that is, the color of the ground on the plant lives,plants growing up in the same environment with the main object, and thelike. Therefore, according to the digital pictorial book system 100, itis possible to search for an explanation of the main object moreprecisely.

FIG. 15 shows a third example of the image database 38 according to thepresent embodiment. In addition to a feature of an object stored in thefirst image database 226 or the second image database 228, the imagedatabase 38 may further store the range of the capture day and/or timeof the object corresponded to an explanation of the object. For example,in case the main object is a flower, the range of the capture day and/ortime may be a period of a year within which the flower blooms.

The annexed information acquiring module 304 acquires informationannexed to the image captured by the image capturing module 100. Forexample, the annexed information acquiring module 304 acquires thecapture day and/or time as annexed information. Then, the featureselecting module 310 selects a feature of a predetermined out of thefeatures of the main object of the kinds extracted by the first featureextracting module 300 and selects annexed information of a predeterminedkind out of information annexed to the image of a plurality of kinds.

The first image database selecting module 322 selects an image database38, which stores the feature of the predetermined kind selected by thefeature selecting module 310 out of the kinds of the features extractedby the first feature extracting module 300 and the annexed informationof the predetermined kind selected by the feature selecting module 310out of the kinds of the features acquired by the annexed informationacquiring module 304, as a first image database. For example, in casethe feature selecting module 310 selects the data and time of imagecapturing as the annexed information of the predetermined kind, thefirst image database selecting module 322 selects an image database 38,which stores an explanation of the object further corresponded to thecapture day and/or time of the object in addition to the feature of thepredetermined kind of the object, out of the plurality of image databases 38, as the first image data base. Further, the first imagedatabase selecting module 322 may select an image database 38, in whichthe first searching module 342 can search for an explanation of the mainobject more precisely by using the feature of the predetermined kindextracted by the first feature extracting module 300 and the annexedinformation of the predetermined kind acquired by the annexedinformation acquiring module 304, with higher priority out of theplurality of the databases 38 as the first image database.

Further, the second image database selecting module 324 selects an imagedatabase 38, which stores a feature of a kind other than thepredetermined kind selected by the feature selecting module 310 out ofthe kinds of the features extracted by the first feature extractingmodule 300 and annexed information of a kind other than thepredetermined kind selected by the feature selecting module 310 out ofthe kinds of the annexed information acquired by the annexed informationacquiring module 304, as a second image database. The second imagedatabase selecting module 324 may select an image database 38, in whichthe second searching module 344 can search for an explanation of themain object more precisely by using the feature of another kindextracted by the first feature extracting module 300 and the annexedinformation of another kind extracted by the annexed informationacquiring module 304, with higher priority out of the plurality of thedatabases 38 as the second image database.

The first searching module 342 searches the name of the main object fromthe first image database selected by the first image database selectingmodule 322 by using both the feature of the predetermined kind selectedby the feature selecting module 310 out of the kinds of the featuresextracted by the first feature extracting module 300 and the annexedinformation of the predetermined kind selected by the feature selectingmodule 310 out of the kinds of the annexed information acquired by theannexed information acquiring module 304. For example, in case thefeature selecting module 310 selects the data and time of imagecapturing as the annexed information of the predetermined kind, thefirst searching module 342 search a name of an object, which correspondsto the feature of the predetermined kind of the object and the rangeincluding the capture day and/or time, from the first image databaseselected by the first database selecting module 322 as the name of themain object. Further, the second searching module 344 searches the nameof the main object from the second image database selected by the secondimage database selecting module 324 by using both a feature of a kindother than the predetermined kind selected by the feature selectingmodule 310 out of the kinds of the features extracted by the firstfeature extracting module 300 and annexed information of a kind otherthan the predetermined kind selected by the feature selecting module 310out of the kinds of the annexed information acquired by the annexedinformation acquiring module 304. As described with regard to FIG. 11,the overlapped object selecting module 350 selects the name of the mainobject on the basis of the results of searching for the name of the mainobject by each of the first searching module 342 and the secondsearching module 344 and outputs the name of the main object to theexplanation retrieving module 370.

According to the digital pictorial book system 10 of the embodiment ofthe present invention, it is possible to search for an explanation ofthe main object more precisely by executing search on the basis of theinformation annexed to the image in addition to the feature of the mainobject.

Further, according to the digital pictorial book system 10, it ispossible to search for an explanation of the main object further on thebasis of the capture day and/or time. For example, in case the mainobject is a flower, it is possible to search for an explanation of theflower which has the feature similar with the main object from flowersblooming at the capture day and/or time out of a plurality of objectsstored in the image database 38. Therefore, it is possible to search foran explanation of the main object more precisely and more efficiently.

Furthermore, at least a part of the plurality of image databases 38 maystore an explanation of an object corresponding to a specified captureday and/or time. Specifically, at least a part of the plurality of imagedatabases 38 may store an explanation of an object corresponding to aspecified season. Here, the range of the data and the time of imagecapturing indicated by the specified season may be stored in the imagedatabase selecting module 320 beforehand or may be acquired by the imagedatabase selecting module 320 from each of at least a part of theplurality of image databases 38 through the communication module 34.

Thus, for the specified range of the day and time, it is possible toreduce the size of the image database 38 without decreasing precision ofsearch, that is, the number of objects which can be searched. Further,in case the size of the image database 38 is the same, it is possible toimprove precision of search. For example, in case the main object is aflower, if an image database 38 stores only the explanation of flowersblooming in a specified season, that is, the “spring,” and the captureday and/or time is included in a period predetermined as the “spring,”it is possible to search for the explanation of a flower of the “spring”by selecting the image database 38 even although the size of the imagedatabase 38 is smaller than an image database storing explanations offlowers regardless of season. Therefore, it is possible to improveconvenience of the user because electronic devices, which have been usedas a digital pictorial book system due to insufficient memory capacity,can be used as a digital pictorial book system which is able to searchfor an explanation of the main object with high precision.

FIG. 16 is a flowchart to show an example of the process by thepictorial book processing module 32 according to the present embodiment.In case the operation mode of the digital pictorial book system 10 isthe “digital pictorial book mode, ” the first feature extracting module300 receives an image captured by the image capturing apparatus 20 andinformation showing a region of a main object selected from the imagefrom the image processing module 130 and extracts features of the mainobject of the image (S1400).

The feature selecting module 310 determines whether or not an imagedatabase storing features of all the kinds extracted by the firstfeature extracting module 300 is included in the plurality of imagedatabases 38 (S1410). In case the image database storing the features ofall the kinds is included in the plurality of image databases 38 (S1410:Yes), the feature selecting module 310 selects the features of all thekinds (S1420).

On the other hand, in case the image database storing the features ofall the kinds is not included in the plurality of image databases 38(S1410: No), the feature selecting module 310 reduces the kinds of thefeatures and selects a part of the features (S1430). Thus, even if thesize of the image database 38, which is available, is small and all theextracted features cannot be used to search for an explanation of themain object, it is possible to execute the search by using the imagedatabase 38. Further, since various image databases 38 can be used forthe search, it is possible to obtain search result covering a widesearch range with high precision.

The image database selecting module 320 selects a first image databasewhich stores the feature of the kind selected by the feature selectingmodule 310 and a second image database which stores a feature of thekind different from the kind selected by the feature selecting module310 (S1440). The first searching module 342 and the second searchingmodule 344 searches a candidate for the name of the main object from thefirst image database and the second image database, respectively (S1450)The candidate name searching module 340 determines whether or not acandidate for the name of the main object is searched out for each imagedatabase 38, that is, whether or not an object is searched out by thefeature of the selected kind in the selected image database 38 (S1460).

In case an object is not searched out by the feature of the selectedkind in the selected image database 38 (S1460: No), the image databaseselecting module 320 determines whether or not all of the selectableimage databases 38 are selected out of the plurality of image databases38 (S1470). In case all of the selectable databases are not selected yet(S1470: No), the image database selecting module 320 returns to S1440and selects one of the selectable image databases 38.

In case all of the selectable image databases 38 are selected, that is,an object is not searched out by the feature extracted by the firstfeature extracting module 300 in none of the plurality of imagedatabases 38 (S1470: Yes), the feature extracting module 310 reduces thekinds of the features and selects a feature again (S1430). Thus, forexample, even if the captured image is of inferior quality and all thekinds of the extracted features cannot be used in order to obtaincorrect search result due to worse capturing conditions, it is possibleto search for an explanation of the main object without requesting theuser to improve the capturing conditions and thus improve convenience ofthe user.

Further, the feature extracting module 310 may except a feature of apredetermined kind among the features extracted by the first featureextracting module 300 from either the kinds of the features used tosearch for the candidate name of the main object in the first imagedatabase by the first searching module 342 or the kinds of the featuresused to search for the candidate name of the main object in the secondimage database by the second searching module 344. Thus, in case asearch success ratio is low for reasons that the feature extracted bythe first feature extracting module 300 has big noises, and the like, itis possible to raise a search success ratio by excluding the featureresulting the low search success ratio.

On the other hand, in case an object is searched out by the feature ofthe selected kind in the selected image database 38 (S1460: Yes), theoverlapped object selecting module 350 selects a name which overlaps ina plurality of image databases 38 out of the candidates for the name ofthe main object searched out in each of the image databases 38 as thename of the main object (S1480). The explanation retrieving module 370retrieves an explanation of the main object on the basis of the nameselected by the overlapped object selecting module 350, displays andprovides the explanation to the user by the display module 50 (S1490).

As described above, according to the digital pictorial book system 10 ofthe embodiment of the present invention, it is possible to search for anexplanation of the main object precisely.

Further, although the digital pictorial book system 10 in the presentfigure searches an explanation of the main object on the basis of onlythe feature of the main object extracted by the first feature extractingmodule 300, the digital pictorial book system 10 may search anexplanation of the main object further on the basis of a feature of anobject other than the main object and information annexed to thecaptured image acquired by the annexed information acquiring module 304.

FIG. 17 is block diagram to show an example of the name determining unit360 according to the present embodiment in detail. An object of the namedetermining unit 360 according to the embodiment of the presentinvention is to determine the name of the main object precisely in casea plurality of candidates are searched out by the candidate namesearching module 340.

The name determining unit 360 includes a partially searched candidateselecting module 362, a re-searching module 364, a probability acquiringmodule 366, and a name determining module 368. In case there is not nocandidate name searched out by the candidate name searching module 340in any of the plurality of image databases 38, the partially searchedcandidate selecting module 362 selects a candidate name, which issearched out in a part of the plurality of image databases 38 and not inthe other image databases 38, and outputs information showing theselected candidate name to the re-searching module 364. The re-searchingmodule 364 searches the image databases 38 by using the communicationmodule 34 to determine whether or not the candidate name selected by thepartially searched candidate selecting module 362 is registered toanother image database 38 and outputs the search result to theprobability acquiring module 366.

The probability acquiring module 366 receives the candidate namessearched out by the candidate name searching module 340 and acquires aprobability of the main object shaving the candidate name for eachcombination of the image database 38 and the candidate name from theimage database 38 through the communication module 34. Here, in case thecandidate name searching module 340 acquires an index value of theprobability of the main object's having the candidate name along withthe search result of the candidate name, the probability acquiringmodule 366 may acquire the index value of the probability from not theimage database 38 but the candidate name searching module 340.

Further, in case there is a candidate name searched out by the candidatename searching module 340 in any of the plurality of image databases 38,the probability acquiring module 366 adjusts the index value of theprobability acquired for each combination of the image database 38 andthe candidate name on the basis of the search result input from there-search module 364. Then, the probability acquiring module 366 outputsthe index value of the probability of the main object's having thecandidate name, which is acquired for each combination of the imagedatabase 38 and the candidate name, to the name determining module 368.

The name determining module 368 determines a most certain name on thebasis of the index value of the probability of the main object's havingeach candidate name received from the probability acquiring module 366.Then, the name determining module 368 outputs the determined name of themain object to the explain retrieving module 370 shown in FIG. 10.

According to the digital pictorial book system of the embodiment of thepresent invention, in case a plurality of the candidate names aresearched out, it is possible to determine the name of the main objectprecisely by using an index value of a probability showing certainty ofeach of the candidate names.

FIG. 18 shows an example of the process by the name determining unit 360according to the present embodiment. FIG. 18A shows the number of namesof objects registered to each image database 38 and candidate namessearched out in the image database 38. For example, the names of objectsregistered in the image database A 38 number 100 and two (2) candidatenames (Lily, Rose) are searched out by the candidate name searchingmodule 340.

FIG. 18B shows an index value of a probability of the main object'shaving each candidate name acquired by the probability acquiring module366. The probability acquiring module 366 acquires the index value ofthe probability of the main object's having each candidate name for eachimage database 38. The index value of the probability of the mainobject's having a candidate name is an index showing to what extent itis certain that the candidate name searched out in the image database 38is the name of the main object. For example, it is desirable that acandidate name searched out has a larger index value when more areliable result is acquired as if a small number of candidate names aresearched out in a large image database 38.

For example, the probability acquiring module 366 acquires a valueacquired by using the number of the names of objects stored beforehandin each image database 38 and the number of the candidate names searchedout in the image database 38, more specifically, a value of the numberof the names of objects stored beforehand in each image database 38divided by the number of the candidate names searched out in the imagedatabase 38, as an index value from the image database 38. Instead, theprobability acquiring module 366 may acquire the index value byacquiring the number of the names of object stored beforehand in eachimage database 38 from the image database 38 and dividing the acquirednumber of the names of object by the number of the candidate namessearched out in the image database 38.

Then, for each candidate name, the probability acquiring module 366acquires the sum of index values of all image databases 38 in which thecandidate name is searched out. For example, in case the candidate nameis “Lily,” the probability acquiring module 366 acquires 200 which isthe sum of the index value 100/2 of the image database A 38 and theindex value 300/2 of the image database B 38.

Here, in case the re-searching module 364 detects that the candidatename selected by the partially searched candidate selecting module 362is registered in another image database 38, the probability acquiringmodule 366 reduces the index value of the probability of the mainobject's having the candidate name. For example, the probabilityacquiring module 366 subtracts an index value acquired by using thenumber of the names of object stored in another image database and thenumber of candidate names searched out in the image database from anindex value of the probability of the main object's having the candidatename selected by the partially searched candidate selecting module 362.Specifically, in case “Water Lily” among the candidate names shown inFIG. 18B is registered in the image database A 38, the probabilityacquiring module 366 adjusts the index vale by subtracting 100/2 whichis an index value for the candidate names searched out in the imagedatabase A 38 from 216 which is the sum of index values for “WaterLily.” Then, the probability acquiring module 366 outputs the sum ofindex values for each candidate name to the name determining module 368.The name determining module 368 determines “Lily,” which is a candidatehaving the largest sum of index values of the candidate names, as thename of the main object.

Further, although the probability acquiring module 366 acquires the sumof index values for each candidate name in the above description, theprobability acquiring module 366 may output each index valueindividually to the name determining module 368. In this case, the namedetermining module 368 may determines a candidate name having thelargest index value as the name of the main object. Further, in case thecandidate name selected by the partially searched candidate selectingmodule 362 is registered in another image database 38, the probabilityacquiring module 366 subtracts a value of an index value for a candidatename searched out in the another image database 38 or the valuemultiplied by a predetermined coefficient from an index value of eachimage database 38 for the candidate name selected by the partiallysearched candidate selecting module 362.

According to the digital pictorial book system 10 of the presentembodiment, it is possible to determine the name of the main objectprecisely by attaching importance to the search result of a larger imagedatabase even if the numbers of the candidate names searched out in aplurality of image databases 38 are the same. Further, according to thedigital pictorial book system 10 of the present embodiment, even if animage database 38 has imprecise data and outputs a candidate nameexcepted from other image databases 38, it is possible to determine themain object precisely by reducing the index value of the probability forthe output of the image databases 38.

FIG. 19 is a flowchart to show an example of a process flow by the namedetermining unit 360. The probability acquiring module 366 acquires anindex value of a probability of the main object's having each of thecandidate names searched out by the candidate name searching module 340for each combination of the image database 38 and the candidate name(S1500).

The name determining unit 360 determines whether or not there is acandidate name searched out in any of the plurality of image databases38 of the candidate names (S1510). In case that there are candidatename(s) searched out in all of the plurality of image databases 38(S1510: Yes), the name determining module 368 determines the mostcertain name of the main object on the basis of the index value of theprobability acquired for each of the candidate name(s) (S1570).

In case there is no candidate name searched out in any of the pluralityof image databases 38 (S1510: No), the name determining unit 360 repeatsthe following processes for each of all the candidate names searched out(S1520).

The partially searched candidate selecting module 362 selects thecandidate name (S1530). The re-search module 364 searches and determineswhether or not the candidate name selected by the partially searchedcandidate selecting module 362 is registered in another image database(S1540). In case the candidate name selected by the partially searchedcandidate selecting module 362 is registered in another image database(S1540: Yes), the probability acquiring module 366 repeats the processesof S1530 to S1550 for all the candidate names searched out (S1560).

Then, the name determining unit 368 determines the most certain name byusing the index value of the probability for each of the candidate namessearched out from a part of the plurality of image databases 38 (S1570).Thus, even if the image databases 38 used to search for the candidatename include an incomplete image database 38 and an image database 38covering a narrow search range, it is possible to determine the name ofthe main object precisely.

FIG. 20 is a block diagram to show an example of the informing unit 380according to the present embodiment. It is an object of the informingunit 380 according to the embodiment of the present invention to improveconvenience of the user, in case a plurality of candidate names aresearched out by the candidate name searching module 340, by advising theuser what image to capture in order to narrow down the candidate namesefficiently.

The informing unit 380 includes a distinguishing feature selectingmodule 382, a featuring part searching module 384, and an informingmodule 386. In case candidate names of a plurality of objects aresearched out by the candidate name searching module 340, thedistinguishing feature selecting module 382 selects a distinguishingfeature, of which an overlap of certainty distributions is the smallestfor each object and each kind of the feature out of the different kindsof features stored in the image database corresponded to the pluralityof objects. Here, the distinguishing feature is specifically a part ofan object, for example, a petal, a leaf, a stamen, and the like, in casethe object is a flower. Then, the distinguishing feature selectingmodule 382 outputs information showing the selected distinguishingfeature to the featuring part searching module 384 and the informingmodule 386.

The featuring part searching module 384 receives the information on thedistinguishing feature from the distinguishing feature selecting module382. Then, the featuring part searching module 384 searches for an imageshowing a part of the main object corresponding to the distinguishingfeature in the image of the main object received from the imageprocessing module 130 and outputs information showing the image, forexample, information showing the region of the image, to the informingmodule 386.

The informing module 386 receives the information showing thedistinguishing feature from the distinguishing feature selecting module382 and the information showing the region of the part corresponding tothe distinguishing feature of the main object from the featuring partsearching module 384. Then, the informing module 386 informs the user ofthe digital pictorial book system 10 of the content of thedistinguishing feature selected by the distinguishing feature selectingmodule 382. Specifically, by outputting the information showing theregion of the part corresponding to the distinguishing feature to theframe display module 40, the informing module 386 displays a frame,which surrounds the part, on the display module 50. Thus, the informingmodule 386 can inform the user that the part should be captured.

According to the informing unit 380 of the embodiment of the presentinvention, it is possible to improve convenience of the user because theinforming module 386 makes the user know how to execute narrow-downefficiently by informing the user which part to capture.

Further, the configuration shown in the present figure is an example andvarious changes can be made to the configuration. For example, theinforming unit 380 may not include the featuring part searching module384. Here, in case the image database 38 has an image of a part showingeach of features of different kinds stored correspondently to aplurality of objects, the informing module 386 may acquire an image of apart having a distinguishing feature from the image database 38 by usingthe communication module 34 on the basis of the information showing thedistinguishing feature received from the distinguishing featureselecting module 382, and display the image on the display module 50.Thus, the image database 38 may inform the user that an image of thepart of the main object, which is the distinguishing feature, should becaptured. Therefore, the user can narrow down the candidate namesefficiently without knowing the name of the part.

FIG. 21 shows an example of certainty distributions of features storedin the image database 38 according to the present embodiment. FIG. 21Ashows an example of a certainty distribution of each object and eachkind of features. The certainty distribution of a feature correspondedto each of objects (Lily, Rose, . . . ) and each kind of features(feature A, feature B, . . . ) shows a relationship between the quantityof feature showing a value of the feature and a probability of theobject's having each of the names. Specifically, the distribution 240shows that a probability of the object's being a lily is P1 in case aquantity of feature extracted of the object is V1.

FIG. 21B shows an overlap of certainty distributions of feature Acorresponding to each of the lily and the rose. FIG. 21C shows anoverlap of certainty distributions of feature B corresponding to each ofthe lily and the rose. As shown in FIG. 21B, the object of which amountof quantity for feature A is V5 includes both the lily and the rose.This is because the overlap of the certainty distributions of the lilyand the rose for feature A is large. Thus, it is difficult for thecandidate name selecting module 340 to distinguish the lily from therose in case of searching by using the feature A.

On the other hand, as shown in FIG. 21C, the object of which amount ofquantity for feature A is V5 includes the lily but does not include therose. This is because the overlap of the certainty distributions of thelily and the rose for feature A is small. Thus, it is possible for thecandidate name selecting module 340 to distinguish the lily from therose in case of searching by using the feature B.

As shown in the present figure, the distinguishing feature selectingmodule 382 acquires the size of the overlap of the certaintydistribution of each object and each feature from the image database 38.Then, in case it is required to distinguish the lily from the rose, thedistinguishing feature selecting module 382 selects feature B as thedistinguishing feature and outputs information showing feature B to thefeaturing part searching module 384 and the informing module 386.

According to the digital pictorial book system 10 of the embodiment ofthe present invention, it is possible to execute narrow-down efficientlyby selecting a distinguishing feature on the basis of an overlap ofcertainty distributions.

FIG. 22 shows a first example of the process of informing by theinforming module 386 according to the present embodiment. According tothe present example, after capturing an image 500 and trying to searchfor an explanation of a main object 502, the user of the digitalpictorial book system 10 is informed by the informing module 386 andcaptures an image 510 once more. As described with regard to FIG. 20,the informing module 386 receives information showing a region of a partcorresponding to a distinguishing feature of a main object included inthe image captured by the image capturing module 100 and displays theimage with the region of the part surrounded by a frame. However, incase an image showing the part is not found in the main object by thefeaturing part searching module 384, the informing unit 386 may informthe user of information showing an image capturing method for capturingan image including the part by using the image capturing module 100.

Here, the information showing the image capturing method may include,for example, information showing the image capturing direction of theimage capturing module 100. For example, in case of searching for anexplanation of the main object 502, which is a flower, of the capturedimage 500, suppose that the distinguishing feature selecting module 382selects a distinguishing feature showing a part, which is a leaf. Inthis case, since the main object 502 of the image 500 does not includean image showing the part which is a leaf, the featuring part searchingmodule 384 cannot search for an image showing a part corresponding tothe distinguishing feature in the main object 502. Further, suppose thatthe image database 38 stores information showing positionalrelationships between parts of the object in the present example. Then,the informing module 386 acquires the positional relationships betweenthe leaf, which is the part corresponding to the selected distinguishingfeature, and parts of which images are included in the main object 502,for example, a petal and a stamen, from the image database 38. Then, theinforming module 386 discovers that the part corresponding to the leafof the main object 502 is below the range of the captured image 500. Theinforming module 386 informs the user that the image capturing directionof the image capturing module 100 should be changed downward, forexample, by displaying an arrow 504 pointing downward on the displaymodule 50 so that the arrow 504 is overlapped with the image 500. Theuser is informed of that, changes the image capturing directiondownward, and makes the image capturing module 100 capture an image 510.Since the main object 512 included in the image 510 includes the leafwhich is the distinguishing feature, the explanation searching module330 can search an explanation of the main object 512 precisely, on thebasis of the distinguishing feature.

Even if a thing which is the main object has a featuring part showing adistinguishing feature which makes the thing clearly distinguished fromanother thing having similar features with the thing, it cannot beconcluded that the user of the digital pictorial book system 10 knowsthe position of the featuring part. Thus, sometimes a captured imagedoes not include the featuring part. However, according to the digitalpictorial book system 10 of the embodiment of the present invention,since it is possible to inform the user of information showing the imagecapturing method for capturing an image including the featuring part,the user can capture an image in which an explanation of the main objectcan be searched out with high precision on the basis of the information.

In addition, even if the featuring part is not included within the imagecapturing range of a captured image, the user can change the imagecapturing direction to capture an image in which an explanation of themain object can be searched out precisely by being informed ofinformation showing the image capturing direction as the informationshowing the image capturing method.

FIG. 23 shows a second example of the process of informing by theinforming module 386 according to the present embodiment. According tothe present example, after capturing an image 520 and trying to searchfor an explanation of a main object 522, the user of the digitalpictorial book system 10 is informed by the informing module 386 andcaptures an image 530 once more. In the following, an example will beexplained, in which information showing the position of the imagecapturing module 100 is included in the information of which theinforming module 386 informs the user and which shows the imagecapturing method for capturing an image comprising the featuring part byusing the image capturing module 100 in case an image showing thefeaturing part is not searched out in the main object by the featuringpart searching module 384.

For example, in case of searching for an explanation of the main object522, which is a flower, of the image 520 captured by the digitalpictorial book system 10, suppose that the distinguishing featureselecting module 382 selects a part corresponding to a stamen from themain object 522 as a distinguishing feature. In this case, since themain object 522 in the image 520 does not comprise an image showing thepart corresponding to the stamen, the featuring part searching module384 cannot search for an image showing the part corresponding to thedistinguishing feature in the main object 522. Further, suppose that theimage database 38, in case the object is viewed from a plurality ofdirections, stores information showing whether or not each part includedin the object is shown for each direction and a feature of each partwhen the object is view from the direction, in the present example. Onthe basis of the information acquired from the image database 38, theinforming module 386 detects the direction in which the main object 522of the image 520 is captured and the difference between the detecteddirection and the direction in which an image comprising the stamen canbe captured, wherein the stamen is the part corresponding to theselected distinguishing feature. Then, the informing module 386concludes that an image including the stamen can be captured if an imageis captured from the direction of being rotated by 90° in the clockwisedirection about the main object 522 as viewed from the above. Then, theinforming module 386 informs the user that the image capturing module100 should be rotated by 90° in the clockwise direction about the mainobject 522, for example, by displaying an arrow 524 showing rotation of90° in the clockwise direction on the display module 50 so that thearrow 524 is overlapped with the image 520. The user is informed ofthat, moves to around the main object in the clockwise direction, andmakes the image capturing module 100 capture an image 530. Since themain object 512 included in the image 530 includes the stamen which isthe distinguishing feature, the explanation searching module 330 cansearch an explanation of the main object 532 precisely, on the basis ofthe distinguishing feature.

Thus, even if the image capturing module 100 captures an image at aplace where an image of a distinguishing feature of the main objectcannot be captured, the user can change the image capturing position bybeing informed of information showing the position of the imagecapturing module 100 as the information showing the image capturingmethod and capture an image in which an explanation of the main objectcan be searched out precisely.

FIG. 24 shows a third example of the process of informing by theinforming module 386 according to the present embodiment. According tothe present example, after capturing an image 540 and trying to searchfor an explanation of a main object 542, the user of the digitalpictorial book system 10 is informed by the informing module 386 andcaptures an image 550 once more. In the following, an example will beexplained, in which information showing image capturing magnification ofthe image capturing module 100 is included in the information of whichthe informing module 386 informs the user and which shows the imagecapturing method for capturing an image comprising the featuring part byusing the image capturing module 100 in case an image showing thefeaturing part is not searched out in the main object by the featuringpart searching module 384.

For example, in case of searching for an explanation of the main object522, which is a flower, of the image 520 captured by the digitalpictorial book system 10, suppose that the distinguishing featureselecting module 382 selects a part corresponding to a stamen from themain object 522 as a distinguishing feature. In this case, the mainobject 542 in the image 540 comprise an image showing the partcorresponding to the stamen, but the size of the image is very small.For this reason, the featuring part searching module 384 cannot searchfor an image showing the part corresponding to the distinguishingfeature in the main object 542. Further, the image database 38 storesinformation showing positional relationships between parts of the objectin the present example, and the informing module 386 acquiresinformation showing the positional relationships between the stamen,which is the part corresponding to the selected distinguishing feature,and a part of which image is included in the main object 542, forexample, a petal, from the image database 38. Then, the informing module386 concludes that the part corresponding to the stamen is in the middleof the petal in the main object 542. The informing module 386 informsthe user that an detailed image of the part which is the stamen shouldbe captured by using the image capturing module 100, for example, bydisplaying an icon image 544 showing increase of the magnification onthe display module 50 so that the icon image 544 is overlapped with theimage 540. The user is informed of that, increases the image capturingmagnification, and makes the image capturing module 100 capture an image550. Since the main object 552 included in the image 550 comprises thestamen which is the part corresponding to the distinguishing feature andthe image of the stamen is so large that the featuring part searchingmodule 384 can search for it, the explanation searching module 330 cansearch an explanation of the main object 552 precisely, on the basis ofthe distinguishing feature.

Thus, even if an image showing a featuring part of the main object istoo small to be searched out, the user can change the image capturingmagnification during capturing an image by being informed of theinformation showing the image capturing magnification of the imagecapturing module 100 as the information showing an image capturingmethod and capture an image in which an explanation of the main objectcan be searched out precisely.

Further, the information showing the image capturing method of which theinforming module 386 informs the user may include diverse informationother than the image capturing direction, the position, and the imagecapturing magnification of the image capturing module 100. For example,the information showing the image capturing method may includeinformation showing capturing conditions such as an exposure time andwhite balance of the image capturing module 100.

FIG. 25 is a flowchart to show an example of a process flow by theinforming unit 380 according to the present embodiment. Thedistinguishing feature selecting module 382 determines whether or not aplurality of candidate names are searched out by the candidate namesearching module 340 (S1600). In case that a plurality of candidatenames are searched out by the candidate name searching module 340(S1600: Yes), for each of a plurality of features, the distinguishingfeature selecting module 382 acquires the size of an overlap ofcertainty distributions for each combination of all the plurality ofobjects searched out from the image database 38 (S1610).

Then, the distinguishing feature selecting module 382 selects adistinguishing feature, of which an overlap on certainty distributionsis the smallest for each combination of all the plurality of objectssearched out (S1620). Here, the distinguishing feature selecting module382 may select the distinguishing feature on the basis of the sum andthe average of the sizes of overlaps. Further, the distinguishingfeature selecting module 382 may select the distinguishing feature onthe basis of the sum and the average of the sizes of overlaps, each ofwhich has different weight for each combination of the objects.

The featuring part searching module 384 searches for a part showing thedistinguishing feature selected by the distinguishing feature selectingmodule 382 in the image of the main object (S1630). For example, thefeaturing part searching module 384 searches for apart showing thedistinguishing feature in the image of the main object by executing apattern matching process using a digitized image pattern of each partshowing the distinguishing feature for a digitized image of the mainobject. Then, the informing module 386 informs the user which part ofthe object to capture by displaying a region showing the partcorresponding to the distinguishing feature searched out by thefeaturing part searching module 384 in the image of the main object soas to be overlapped with the image of the main object on the displaymodule 50 by using the frame display module (S1640).

As described above, according to the digital pictorial book system 10 ofthe embodiment of the present invention, since it is possible for theuser to understand that a part of the object before his/her eyes is thedistinguishing feature, the user can execute narrow-down of thecandidate names easily and efficiently.

The informing module 386 in FIGS. 20 to 25 displays a frame surroundinga part corresponding the distinguishing feature selected by thedistinguishing feature selecting module 382 on the display module 50 toinform the user of the part, or displays an arrow and an icon imageshowing an image capturing method on the display module 50 to inform theuser of the image capturing method. Instead, the informing module 386may inform the user of the content of the selected distinguishingfeature by using a voice. For example, the informing module 386 mayretrieve voice data showing the name of the part corresponding to theselected distinguishing feature from the image database 38 and output avoice such as “Please capture an image of the leaf.” Which is the voicedata showing the name of the part corresponding to the selecteddistinguishing feature through a voice output module such as a speakerprovided in the digital pictorial book system 10. Further, the informingmodule 386 may inform the user of the image capturing method forcapturing an image including the part corresponding to the selecteddistinguishing feature making use of the image capturing module 100 byusing a voice. For example, in case the image 500 shown in FIG. 22 iscaptured, the informing module 386 may output a voice of “Please capturean image of the lower part.” through the voice output module such as aspeaker. Thus, since the informing module informs the user informationby a voice, even a user of the digital pictorial book system 10, who haslittle experience in handling electronic devices, can easily understandthe content of the information.

Further, instead the informing module 386 informs the user of theinformation showing the image capturing method of the image capturingmodule 10 on the basis of the content of the distinguishing featureselected by the distinguishing feature selecting module 382 by thedisplay module 50, the image capture controlling module 120 may controlthe operation of the image capturing module 100 on the basis of theselected distinguishing feature. Specifically, the image capturecontrolling module 120 may receive an image, which shows a partcorresponding to a distinguishing feature and is searched out by thefeaturing part searching module 384, and control the operation of theimage capturing module 100 on the basis of the information showing thedistinguishing feature selected by the distinguishing feature selectingmodule 382, the received distinguishing feature and image. Thus, it ispossible to search for explanation of the main object by automaticallycontrolling the operation of the image capturing module 100 andcapturing an image of the selected distinguishing feature or an imageincluding a part corresponding to the distinguishing feature without theuser's operating the image capturing apparatus 20 on the basis of theinformation by the informing module 386.

For example, in case a part corresponding to a distinguishing feature isnot searched out in a main object included in an image by the featuringpart searching module 384, the image capture controlling module 120 maycontrol the image capturing direction of the image capturing module 100so that the part is included in the image capturing range. For example,in case the main object 502 included in the captured image 500 does notinclude a part which is the leaf corresponding to the distinguishingfeature as shown in FIG. 22, the image capture controlling module 120may detect a position of the part which is the leaf and change the imagecapturing direction of the image capturing module 100 downwardly so thatthe part which is the leaf is included in the image capturing rangesimilarly to the informing module 386 described with regard to FIG. 22.In this case, the image capture controlling module 120 may control theimage capturing direction of the image capturing module 100 by drivingan actuator provided in the image capturing module 100, for example, soas to control the direction of the optical axis of the optical system102. Thus, even if the captured image does not include a partcorresponding to a distinguishing feature which is a featuring part ofthe main object, explanation of the main object can be searched outprecisely without imposing a burden on the user because it is possibleto automatically control the image capturing direction and capture animage including the part.

Further, for example, in case a part corresponding to a distinguishingfeature is not searched out in a main object included in an image by thefeaturing part searching module 384, the image capture controllingmodule 120 may control the image capturing range of the image capturingmodule 100 so that the part is included in the image capturing range.For example, in case the main object 522 included in the captured image520 does not include a part which is the stamen corresponding to thedistinguishing feature as shown in FIG. 23, the image capturecontrolling module 120 may detect a direction in which an imageincluding the part which is the stamen can be captured and move theimage capturing module 100 around the main object 522 by 90° in theclockwise direction as vied from the above so that the part which is thestamen is included in the image capturing range similarly to theinforming module 386 described with regard to FIG. 23. Here, at least apart of the digital pictorial book system 10, which includes the imagecapturing module 100, may be formed mobile by such as a wheel and motoror may be provided in a small helicopter, and the image capturecontrolling module 120 may control at least a part of place anddirection. Thus, even if the user captures an image from a direction inwhich a distinguishing feature which is a featuring part of the mainobject cannot be captured, explanation of the main object can besearched out precisely without imposing a burden on the user because itis possible to automatically control the image capturing direction andcapture an image including the part.

Furthermore, for example, in case a ratio of the size of an imageshowing a part corresponding to a distinguishing feature searched out inan image captured by the image capturing module 100 by the featuringpart searching module 384 to the whole size of the captured image issmaller than a predetermined value, the image capture controlling module120 may increase the image capturing magnification of the imagecapturing module 100 so that the part is included in the image capturingrange. For example, the featuring part searching module 384 can searchfor an image showing a part corresponding to a distinguishing feature onthe basis of general features included in the captured image but cannotdetect the distinguishing feature detailed enough to narrow a pluralityof candidate objects to the main object from the image. Then, in casethe ratio of the size of the part of the image which is search out tothe whole size of the captured image is smaller than a predeterminedvalue, the image capture controlling module 120 increases the imagecapturing magnification of the image capturing module 100 more accordingto the ratio. Further, the predetermined value may be a typical value ofa ratio of an image, which shows a part corresponding to adistinguishing feature, to the whole captured image in case thedistinguishing feature is detailed enough that the explanation searchingmodule 330 can search for an explanation of the main object withsufficiently high precision and the distinguishing feature can bedetected from the captured image, or predetermined by the user. Thus,even if the user cannot capture an image of a featuring of the mainobject, explanation of the main object can be searched out preciselywithout imposing a burden on the user by automatically increasing theimage capturing magnification of the image capturing module 100 andcapturing an image including the part of a sufficiently big size.

The image database 38 may further store dangerous thing information,which shows whether or not the object is a highly dangerous thing,corresponded to a plurality of different kinds of features of theobjects. In this case, the informing module may inform the user that theobject is a highly dangerous thing by, for example, displaying anexpression “Danger” on the display module 50 in case it is shown by thedangerous thing information corresponded to the object of which theexplanation and the candidate name are searched out by the explanationsearching module 330. Thus, the user can be prevented from coming nearan animal or a plant without knowing that the animal or the plant ishighly dangerous so as to capture an image which shows a featuring partof the animal or the plant in detail.

As described above, according to the digital pictorial book system 10 ofthe embodiment of the present invention, the image capturing apparatus20, the pictorial book processing module 32, and the communicatingmodule may be formed as a single device or may as a plurality of devicesconnected each other. Further, in case the digital pictorial book system10 is formed as a single device, the image capturing apparatus 20, whichis, for example, a digital still camera or a digital video camera, mayinclude the pictorial book processing module 32 and the communicationmodule 34 explained with regard to FIGS. 10 to 25.

Here, in case of extracting a feature of an object other than the mainobject, the second feature extracting module 302 shown in FIG. 10 mayextract a feature of the object from an image captured at a focal lengthof a region including the object, which is acquired by the focal lengthacquiring module 144, out of a plurality of images which the repeatedlycapturing module 142 shown in FIG. 2 makes the image capturing module100 capture while varying the focal length. Thus, since the secondfeature extracting module 302 can extract a feature of the object froman image which is clear and has a high focusing degree, the digitalpictorial book system 10 can search for an explanation of the mainobject more precisely.

Further, the main object distance acquiring module 146 may acquire themain object distance for each of regions included in the image on thebasis of whether or not a feature of the same kind as a feature of theobject stored in the image database 38 is included in the region. Thus,in case the usable image database 38 stores only explanation on anobject of a predetermined kind, for example, a flower, the main objectdistance acquiring module 146 can select the object of the predeterminedkind included in the captured image, specifically, the object having afeature of the same kind as the feature of the object of thepredetermined kind, as the main object. Therefore, in case the user usesthe image database 38 related to an object of a predetermined kind so asto search for an explanation of the object of the predetermined kind, itis possible to select an object, of which explanation the user desiresto search for, as the main object precisely.

FIG. 26 is a block diagram to show an example of the hardwareconfiguration of a personal computer 70 performing a function as thedigital pictorial book system 10 according to the embodiment of thepresent invention. The personal computer 70 includes a CPU 700, a ROM702, a RAM 704, a communication interface 706, a hard disk drive 710, aflexible disk drive 712, and a CD-ROM drive 714. The CPU 700 operatesbased on programs stored in the ROM 702 and the RAM 704 and controlseach part of the personal computer 70.

The flexible disk drive 712 retrieves data or a program from a flexibledisk 720 and stores them in the RAM 704. The CD-ROM drive 714 retrievesdata or a program from a CD-ROM 722 and stores them in the RAM 704.

A program is stored on a recording medium such as the flexible disk 720or the CD-ROM 722 and provided to the digital pictorial book system 10by the user. The program stored in the recording medium may becompressed or not. The program is retrieved from the recording medium,installed in the digital pictorial book system 10, and executed in thedigital pictorial book system 10. The program, which is provided by therecording medium and installed in the digital pictorial book system 10,executes the functions of the digital pictorial book system 10 describedwith regard to the FIGS. 1 to 25.

It is possible to store a part or all of the processes of the digitalpictorial book system 10 according to the embodiment explained in thepresent application in the flexible disk 720 or the CD-ROM 722 shown inFIG. 26, which is an example of the recording medium.

The program may be directly retrieved from the recording medium andexecuted by the digital pictorial book system 10, or may be executedafter being installed in the digital pictorial book system 10. Further,the program may be stored in one or a plurality of recording medium(s).Furthermore, the program may be stored in an encoded form.

An optical recoding medium such as DVD, PD, etc., a magneto-opticalrecording medium such as MD, a tape medium, a magnetic recoding medium,a semiconductor memory such as an IC card, and a miniature card can beused as a recoding medium in addition to the flexible disk and theCD-ROM. Further, a storing apparatus such as a hard disk or a RAMprovided in a server system connected with a dedicated communicationnetwork and the Internet may be used as the recording medium and providethe digital pictorial book system with the program through acommunication network.

According to the present invention, in case of executing search by usinga plurality of image database, it is possible to obtain a search resultwith high precision and efficiently.

Although the present invention has been described by way of exemplaryembodiments, it should be understood that those skilled in the art mightmake many changes and substitutions without departing from the spiritand the scope of the present invention which is defined only by theappended claims.

1. A digital pictorial book system for searching for and providing auser with an explanation of an object captured by an image capturingmodule comprising: an image capturing module for capturing an image; amain object selecting module for selecting a main object out of theimage; a feature extracting module for extracting a feature of the mainobject; an explanation searching module for searching for theexplanation of the main object in a plurality of image databases, whichstore explanations of the objects corresponded to a plurality ofdifferent kinds of features of the subjects by using the featureextracted by said feature extracting module; and a distinguishingfeature selecting module for selecting a distinguishing feature, ofwhich an overlap of certainty distributions is the smallest for eachobject and each kind of the feature out of the different kinds offeatures stored in the image database corresponded to the plurality ofobjects in case the explanations of the plurality of objects aresearched out by said explanation searching module.
 2. A digitalpictorial book system as claimed in claim 1 further comprising a secondfeature extracting module for extracting a feature of an object otherthan the main object out of the image, wherein said explanationsearching module searches for the explanation of the main object byusing the feature extracted by said second feature extracting module inthe plurality of image databases which store the explanation of theobject further corresponded to the feature of a thing of high relevancewith the object.
 3. A digital pictorial book system as claimed in claim1 further comprising annexed information acquiring module for acquiringannexed information annexed to the image, wherein explanation searchingmodule searches for the explanation of the main object by using theannexed information acquired by said annexed information acquiringmodule in the plurality of image databases which store the explanationof the object further corresponded to the annexed informationcorresponded to the image of the object.
 4. A digital pictorial booksystem as claimed in claim 1 further comprising an informing module forinforming the user of said digital pictorial book system, of a contentof the distinguishing feature selected by said distinguishing featureselecting module.
 5. A digital pictorial book system as claimed in claim4, wherein said distinguishing feature shows a part of the object, andsaid informing module informs the user that the part of the main objectshould be captured.
 6. A digital pictorial book system as claimed inclaim 5 further comprising a display module for displaying the imagecaptured by said image capturing module, wherein said image databasecomprises an image of each part of the object as a feature, and saidinforming module displays the image of the part, which is thedistinguishing feature, on said display module.
 7. A digital pictorialbook system as claimed in claim 5 further comprising a featuring partsearching module for searching for the image showing the part of themain object, wherein said informing module displays a frame whichsurrounds the part on said displaying module.
 8. A digital pictorialbook system as claimed in claim 7, wherein said informing module informsthe user of information showing an image capturing method for capturingthe image comprising the part by using said image capturing module incase the image showing the part is searched out in the main object bysaid featuring part searching module.
 9. A digital pictorial book systemas claimed in claim 8, wherein the information showing the imagecapturing method comprises information showing the image capturingdirection of said image capturing module.
 10. A digital pictorial booksystem as claimed in claim 8, wherein the information showing the imagecapturing method comprises information showing a position of said imagecapturing module.
 11. A digital pictorial book system as claimed inclaim 8, wherein the information showing the image capturing methodcomprises information showing image capturing magnification of saidimage capturing module.
 12. A digital pictorial book system as claimedin claim 5, wherein said informing module informs the user of thedigital pictorial book system of the content of the distinguishingfeature selected by said distinguishing feature selecting module byusing a voice.
 13. A digital pictorial book system as claimed in claim 1further comprising an image capturing control module for controlling theoperation of said image capturing module on the basis of the content ofthe distinguishing feature selected by said distinguishing featureselecting module.
 14. A digital pictorial book system as claimed inclaim 13 further comprising a featuring part searching module forsearching for an image showing a part of the main object, wherein thedistinguishing feature shows the part of the object, and said imagecapturing control module controls the operation of said image capturingmodule on the basis of the image showing the part searched out by saidfeaturing part searching module.
 15. A digital pictorial book system asclaimed in claim 14, wherein said image capturing control modulecontrols the image capturing direction of said image capturing module inorder for the part to be comprised in the image capturing range of saidimage capturing module in case the image showing the part is notsearched out by said featuring part searching module in the main object.16. A digital pictorial book system as claimed in claim 14, wherein saidimage capturing control module controls the position of said imagecapturing module in order for the part to be comprised in the imagecapturing range of said image capturing module in case the image showingthe part is not searched out by said featuring part searching module inthe main object.
 17. A digital pictorial book system as claimed in claim14, wherein said image capturing control module increases the imagecapturing magnification of said image capturing module in case a ratioof a size of the image showing the part searched out by the featuringpart searching module to that of the whole image captured by said imagecapturing module is smaller than a predetermined reference value.
 18. Adigital pictorial book system as claimed in claim 5, wherein said imagedatabase further stores a dangerous thing information, which showswhether or not the object is a highly dangerous thing, corresponded to aplurality of different kinds of features of the objects, and saidinforming module informs the user that the object is a highly dangerousthing in case it is shown by the dangerous thing informationcorresponded to the object of which the explanation is searched out bysaid explanation searching module.
 19. A digital pictorial booksearching method performed by a digital pictorial book system forsearching for and providing a user with an explanation of an objectcaptured by an image capturing module comprising: an image capturingstep of capturing an image; a main object selecting step of selecting amain object out of the image; a feature extracting step of extracting afeature of the main object; an explanation searching step of searchingfor the explanation of the main object in a plurality of imagedatabases, which store explanations of the objects corresponded to aplurality of different kinds of features of the objects by using thefeature extracted by said feature extracting step; a distinguishingfeature selecting step of selecting a distinguishing feature, of whichan overlap of certainty distributions is the smallest for each objectand each kind of the feature out of the different kinds of featuresstored in the image database corresponded to the plurality of objects incase the explanations of the plurality of objects are searched out bysaid explanation searching step; and an informing step of informing theuser of said digital pictoria. book system, of a content of thedistinguishing feature selected by said distinguishing feature selectingstep.
 20. A digital pictorial book searching method as claimed in claim19 further comprising a featuring part searching step of searching forthe image showing the part of the main object, wherein, during saidinforming step, a frame which surrounds the part is displayed in saiddisplaying step.
 21. A machine readable medium storing thereon acomputer program making a computer perform as a digital pictorial booksystem for searching for and providing a user with an explanation of anobject captured by an image capturing module, said digital pictorialbook system comprising: an image capturing module for capturing animage; a main object selecting module for selecting a main object out ofthe image; a feature extracting module for extracting a feature of themain object; an explanation searching module for searching for theexplanation of the main object in a plurality of image databases, whichstore explanations of the objects corresponded to a plurality ofdifferent kinds of features of the objects by using the featureextracted by said feature extracting module; a distinguishing featureselecting module for selecting a distinguishing feature, of which anoverlap of certainty distributions is the smallest for each object andeach kind of the feature out of the different kinds of features storedin the image database corresponded to the plurality of objects in casethe explanations of the plurality of objects are searched out by saidexplanation searching module; and an informing module for informing theuser of said digital pictorial book system, of a content of thedistinguishing feature selected by said distinguishing feature selectingmodule.
 22. A machine readable medium as claimed in claim 21 furthercomprising a featuring part searching module for searching for the imageshowing the part of the main object, wherein said informing moduledisplays a frame which surrounds the part on said displaying module.